回传数据解析，兼容v5和v10

ultralytics/trackers/utils/__init__.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license

ultralytics/trackers/utils/gmc.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import copy
+
+import cv2
+import numpy as np
+
+from ultralytics.utils import LOGGER
+
+
+class GMC:
+    """
+    Generalized Motion Compensation (GMC) class for tracking and object detection in video frames.
+
+    This class provides methods for tracking and detecting objects based on several tracking algorithms including ORB,
+    SIFT, ECC, and Sparse Optical Flow. It also supports downscaling of frames for computational efficiency.
+
+    Attributes:
+        method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
+        downscale (int): Factor by which to downscale the frames for processing.
+        prevFrame (np.ndarray): Stores the previous frame for tracking.
+        prevKeyPoints (list): Stores the keypoints from the previous frame.
+        prevDescriptors (np.ndarray): Stores the descriptors from the previous frame.
+        initializedFirstFrame (bool): Flag to indicate if the first frame has been processed.
+
+    Methods:
+        __init__(self, method='sparseOptFlow', downscale=2): Initializes a GMC object with the specified method
+                                                              and downscale factor.
+        apply(self, raw_frame, detections=None): Applies the chosen method to a raw frame and optionally uses
+                                                 provided detections.
+        applyEcc(self, raw_frame, detections=None): Applies the ECC algorithm to a raw frame.
+        applyFeatures(self, raw_frame, detections=None): Applies feature-based methods like ORB or SIFT to a raw frame.
+        applySparseOptFlow(self, raw_frame, detections=None): Applies the Sparse Optical Flow method to a raw frame.
+    """
+
+    def __init__(self, method: str = "sparseOptFlow", downscale: int = 2) -> None:
+        """
+        Initialize a video tracker with specified parameters.
+
+        Args:
+            method (str): The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'.
+            downscale (int): Downscale factor for processing frames.
+        """
+        super().__init__()
+
+        self.method = method
+        self.downscale = max(1, int(downscale))
+
+        if self.method == "orb":
+            self.detector = cv2.FastFeatureDetector_create(20)
+            self.extractor = cv2.ORB_create()
+            self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
+
+        elif self.method == "sift":
+            self.detector = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
+            self.extractor = cv2.SIFT_create(nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
+            self.matcher = cv2.BFMatcher(cv2.NORM_L2)
+
+        elif self.method == "ecc":
+            number_of_iterations = 5000
+            termination_eps = 1e-6
+            self.warp_mode = cv2.MOTION_EUCLIDEAN
+            self.criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)
+
+        elif self.method == "sparseOptFlow":
+            self.feature_params = dict(
+                maxCorners=1000, qualityLevel=0.01, minDistance=1, blockSize=3, useHarrisDetector=False, k=0.04
+            )
+
+        elif self.method in {"none", "None", None}:
+            self.method = None
+        else:
+            raise ValueError(f"Error: Unknown GMC method:{method}")
+
+        self.prevFrame = None
+        self.prevKeyPoints = None
+        self.prevDescriptors = None
+        self.initializedFirstFrame = False
+
+    def apply(self, raw_frame: np.array, detections: list = None) -> np.array:
+        """
+        Apply object detection on a raw frame using specified method.
+
+        Args:
+            raw_frame (np.ndarray): The raw frame to be processed.
+            detections (list): List of detections to be used in the processing.
+
+        Returns:
+            (np.ndarray): Processed frame.
+
+        Examples:
+            >>> gmc = GMC()
+            >>> gmc.apply(np.array([[1, 2, 3], [4, 5, 6]]))
+            array([[1, 2, 3],
+                   [4, 5, 6]])
+        """
+        if self.method in ["orb", "sift"]:
+            return self.applyFeatures(raw_frame, detections)
+        elif self.method == "ecc":
+            return self.applyEcc(raw_frame)
+        elif self.method == "sparseOptFlow":
+            return self.applySparseOptFlow(raw_frame)
+        else:
+            return np.eye(2, 3)
+
+    def applyEcc(self, raw_frame: np.array) -> np.array:
+        """
+        Apply ECC algorithm to a raw frame.
+
+        Args:
+            raw_frame (np.ndarray): The raw frame to be processed.
+
+        Returns:
+            (np.ndarray): Processed frame.
+
+        Examples:
+            >>> gmc = GMC()
+            >>> gmc.applyEcc(np.array([[1, 2, 3], [4, 5, 6]]))
+            array([[1, 2, 3],
+                   [4, 5, 6]])
+        """
+        height, width, _ = raw_frame.shape
+        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
+        H = np.eye(2, 3, dtype=np.float32)
+
+        # Downscale image
+        if self.downscale > 1.0:
+            frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
+            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
+            width = width // self.downscale
+            height = height // self.downscale
+
+        # Handle first frame
+        if not self.initializedFirstFrame:
+            # Initialize data
+            self.prevFrame = frame.copy()
+
+            # Initialization done
+            self.initializedFirstFrame = True
+
+            return H
+
+        # Run the ECC algorithm. The results are stored in warp_matrix.
+        # (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria)
+        try:
+            (_, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria, None, 1)
+        except Exception as e:
+            LOGGER.warning(f"WARNING: find transform failed. Set warp as identity {e}")
+
+        return H
+
+    def applyFeatures(self, raw_frame: np.array, detections: list = None) -> np.array:
+        """
+        Apply feature-based methods like ORB or SIFT to a raw frame.
+
+        Args:
+            raw_frame (np.ndarray): The raw frame to be processed.
+            detections (list): List of detections to be used in the processing.
+
+        Returns:
+            (np.ndarray): Processed frame.
+
+        Examples:
+            >>> gmc = GMC()
+            >>> gmc.applyFeatures(np.array([[1, 2, 3], [4, 5, 6]]))
+            array([[1, 2, 3],
+                   [4, 5, 6]])
+        """
+        height, width, _ = raw_frame.shape
+        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
+        H = np.eye(2, 3)
+
+        # Downscale image
+        if self.downscale > 1.0:
+            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
+            width = width // self.downscale
+            height = height // self.downscale
+
+        # Find the keypoints
+        mask = np.zeros_like(frame)
+        mask[int(0.02 * height) : int(0.98 * height), int(0.02 * width) : int(0.98 * width)] = 255
+        if detections is not None:
+            for det in detections:
+                tlbr = (det[:4] / self.downscale).astype(np.int_)
+                mask[tlbr[1] : tlbr[3], tlbr[0] : tlbr[2]] = 0
+
+        keypoints = self.detector.detect(frame, mask)
+
+        # Compute the descriptors
+        keypoints, descriptors = self.extractor.compute(frame, keypoints)
+
+        # Handle first frame
+        if not self.initializedFirstFrame:
+            # Initialize data
+            self.prevFrame = frame.copy()
+            self.prevKeyPoints = copy.copy(keypoints)
+            self.prevDescriptors = copy.copy(descriptors)
+
+            # Initialization done
+            self.initializedFirstFrame = True
+
+            return H
+
+        # Match descriptors
+        knnMatches = self.matcher.knnMatch(self.prevDescriptors, descriptors, 2)
+
+        # Filter matches based on smallest spatial distance
+        matches = []
+        spatialDistances = []
+
+        maxSpatialDistance = 0.25 * np.array([width, height])
+
+        # Handle empty matches case
+        if len(knnMatches) == 0:
+            # Store to next iteration
+            self.prevFrame = frame.copy()
+            self.prevKeyPoints = copy.copy(keypoints)
+            self.prevDescriptors = copy.copy(descriptors)
+
+            return H
+
+        for m, n in knnMatches:
+            if m.distance < 0.9 * n.distance:
+                prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
+                currKeyPointLocation = keypoints[m.trainIdx].pt
+
+                spatialDistance = (
+                    prevKeyPointLocation[0] - currKeyPointLocation[0],
+                    prevKeyPointLocation[1] - currKeyPointLocation[1],
+                )
+
+                if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and (
+                    np.abs(spatialDistance[1]) < maxSpatialDistance[1]
+                ):
+                    spatialDistances.append(spatialDistance)
+                    matches.append(m)
+
+        meanSpatialDistances = np.mean(spatialDistances, 0)
+        stdSpatialDistances = np.std(spatialDistances, 0)
+
+        inliers = (spatialDistances - meanSpatialDistances) < 2.5 * stdSpatialDistances
+
+        goodMatches = []
+        prevPoints = []
+        currPoints = []
+        for i in range(len(matches)):
+            if inliers[i, 0] and inliers[i, 1]:
+                goodMatches.append(matches[i])
+                prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
+                currPoints.append(keypoints[matches[i].trainIdx].pt)
+
+        prevPoints = np.array(prevPoints)
+        currPoints = np.array(currPoints)
+
+        # Draw the keypoint matches on the output image
+        # if False:
+        #     import matplotlib.pyplot as plt
+        #     matches_img = np.hstack((self.prevFrame, frame))
+        #     matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
+        #     W = self.prevFrame.shape[1]
+        #     for m in goodMatches:
+        #         prev_pt = np.array(self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
+        #         curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
+        #         curr_pt[0] += W
+        #         color = np.random.randint(0, 255, 3)
+        #         color = (int(color[0]), int(color[1]), int(color[2]))
+        #
+        #         matches_img = cv2.line(matches_img, prev_pt, curr_pt, tuple(color), 1, cv2.LINE_AA)
+        #         matches_img = cv2.circle(matches_img, prev_pt, 2, tuple(color), -1)
+        #         matches_img = cv2.circle(matches_img, curr_pt, 2, tuple(color), -1)
+        #
+        #     plt.figure()
+        #     plt.imshow(matches_img)
+        #     plt.show()
+
+        # Find rigid matrix
+        if prevPoints.shape[0] > 4:
+            H, inliers = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
+
+            # Handle downscale
+            if self.downscale > 1.0:
+                H[0, 2] *= self.downscale
+                H[1, 2] *= self.downscale
+        else:
+            LOGGER.warning("WARNING: not enough matching points")
+
+        # Store to next iteration
+        self.prevFrame = frame.copy()
+        self.prevKeyPoints = copy.copy(keypoints)
+        self.prevDescriptors = copy.copy(descriptors)
+
+        return H
+
+    def applySparseOptFlow(self, raw_frame: np.array) -> np.array:
+        """
+        Apply Sparse Optical Flow method to a raw frame.
+
+        Args:
+            raw_frame (np.ndarray): The raw frame to be processed.
+
+        Returns:
+            (np.ndarray): Processed frame.
+
+        Examples:
+            >>> gmc = GMC()
+            >>> gmc.applySparseOptFlow(np.array([[1, 2, 3], [4, 5, 6]]))
+            array([[1, 2, 3],
+                   [4, 5, 6]])
+        """
+        height, width, _ = raw_frame.shape
+        frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
+        H = np.eye(2, 3)
+
+        # Downscale image
+        if self.downscale > 1.0:
+            frame = cv2.resize(frame, (width // self.downscale, height // self.downscale))
+
+        # Find the keypoints
+        keypoints = cv2.goodFeaturesToTrack(frame, mask=None, **self.feature_params)
+
+        # Handle first frame
+        if not self.initializedFirstFrame:
+            self.prevFrame = frame.copy()
+            self.prevKeyPoints = copy.copy(keypoints)
+            self.initializedFirstFrame = True
+            return H
+
+        # Find correspondences
+        matchedKeypoints, status, _ = cv2.calcOpticalFlowPyrLK(self.prevFrame, frame, self.prevKeyPoints, None)
+
+        # Leave good correspondences only
+        prevPoints = []
+        currPoints = []
+
+        for i in range(len(status)):
+            if status[i]:
+                prevPoints.append(self.prevKeyPoints[i])
+                currPoints.append(matchedKeypoints[i])
+
+        prevPoints = np.array(prevPoints)
+        currPoints = np.array(currPoints)
+
+        # Find rigid matrix
+        if (prevPoints.shape[0] > 4) and (prevPoints.shape[0] == prevPoints.shape[0]):
+            H, _ = cv2.estimateAffinePartial2D(prevPoints, currPoints, cv2.RANSAC)
+
+            if self.downscale > 1.0:
+                H[0, 2] *= self.downscale
+                H[1, 2] *= self.downscale
+        else:
+            LOGGER.warning("WARNING: not enough matching points")
+
+        self.prevFrame = frame.copy()
+        self.prevKeyPoints = copy.copy(keypoints)
+
+        return H
+
+    def reset_params(self) -> None:
+        """Reset parameters."""
+        self.prevFrame = None
+        self.prevKeyPoints = None
+        self.prevDescriptors = None
+        self.initializedFirstFrame = False

ultralytics/trackers/utils/kalman_filter.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import numpy as np
+import scipy.linalg
+
+
+class KalmanFilterXYAH:
+    """
+    For bytetrack. A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y), aspect
+    ratio a, height h, and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location (x, y, a, h) is taken as direct
+    observation of the state space (linear observation model).
+    """
+
+    def __init__(self):
+        """Initialize Kalman filter model matrices with motion and observation uncertainty weights."""
+        ndim, dt = 4, 1.0
+
+        # Create Kalman filter model matrices
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current state estimate. These weights control
+        # the amount of uncertainty in the model.
+        self._std_weight_position = 1.0 / 20
+        self._std_weight_velocity = 1.0 / 160
+
+    def initiate(self, measurement: np.ndarray) -> tuple:
+        """
+        Create track from unassociated measurement.
+
+        Args:
+            measurement (ndarray): Bounding box coordinates (x, y, a, h) with center position (x, y), aspect ratio a,
+                and height h.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+                the new track. Unobserved velocities are initialized to 0 mean.
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3],
+        ]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
+        """
+        Run Kalman filter prediction step.
+
+        Args:
+            mean (ndarray): The 8 dimensional mean vector of the object state at the previous time step.
+            covariance (ndarray): The 8x8 dimensional covariance matrix of the object state at the previous time step.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
+                velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3],
+        ]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3],
+        ]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(mean, self._motion_mat.T)
+        covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
+        """
+        Project state distribution to measurement space.
+
+        Args:
+            mean (ndarray): The state's mean vector (8 dimensional array).
+            covariance (ndarray): The state's covariance matrix (8x8 dimensional).
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the projected mean and covariance matrix of the given state estimate.
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3],
+        ]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def multi_predict(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
+        """
+        Run Kalman filter prediction step (Vectorized version).
+
+        Args:
+            mean (ndarray): The Nx8 dimensional mean matrix of the object states at the previous time step.
+            covariance (ndarray): The Nx8x8 covariance matrix of the object states at the previous time step.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
+                velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[:, 3],
+            self._std_weight_position * mean[:, 3],
+            1e-2 * np.ones_like(mean[:, 3]),
+            self._std_weight_position * mean[:, 3],
+        ]
+        std_vel = [
+            self._std_weight_velocity * mean[:, 3],
+            self._std_weight_velocity * mean[:, 3],
+            1e-5 * np.ones_like(mean[:, 3]),
+            self._std_weight_velocity * mean[:, 3],
+        ]
+        sqr = np.square(np.r_[std_pos, std_vel]).T
+
+        motion_cov = [np.diag(sqr[i]) for i in range(len(mean))]
+        motion_cov = np.asarray(motion_cov)
+
+        mean = np.dot(mean, self._motion_mat.T)
+        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
+        covariance = np.dot(left, self._motion_mat.T) + motion_cov
+
+        return mean, covariance
+
+    def update(self, mean: np.ndarray, covariance: np.ndarray, measurement: np.ndarray) -> tuple:
+        """
+        Run Kalman filter correction step.
+
+        Args:
+            mean (ndarray): The predicted state's mean vector (8 dimensional).
+            covariance (ndarray): The state's covariance matrix (8x8 dimensional).
+            measurement (ndarray): The 4 dimensional measurement vector (x, y, a, h), where (x, y) is the center
+                position, a the aspect ratio, and h the height of the bounding box.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the measurement-corrected state distribution.
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T, check_finite=False
+        ).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        new_covariance = covariance - np.linalg.multi_dot((kalman_gain, projected_cov, kalman_gain.T))
+        return new_mean, new_covariance
+
+    def gating_distance(
+        self,
+        mean: np.ndarray,
+        covariance: np.ndarray,
+        measurements: np.ndarray,
+        only_position: bool = False,
+        metric: str = "maha",
+    ) -> np.ndarray:
+        """
+        Compute gating distance between state distribution and measurements. A suitable distance threshold can be
+        obtained from `chi2inv95`. If `only_position` is False, the chi-square distribution has 4 degrees of freedom,
+        otherwise 2.
+
+        Args:
+            mean (ndarray): Mean vector over the state distribution (8 dimensional).
+            covariance (ndarray): Covariance of the state distribution (8x8 dimensional).
+            measurements (ndarray): An Nx4 matrix of N measurements, each in format (x, y, a, h) where (x, y)
+                is the bounding box center position, a the aspect ratio, and h the height.
+            only_position (bool, optional): If True, distance computation is done with respect to the bounding box
+                center position only. Defaults to False.
+            metric (str, optional): The metric to use for calculating the distance. Options are 'gaussian' for the
+                squared Euclidean distance and 'maha' for the squared Mahalanobis distance. Defaults to 'maha'.
+
+        Returns:
+            (np.ndarray): Returns an array of length N, where the i-th element contains the squared distance between
+                (mean, covariance) and `measurements[i]`.
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        d = measurements - mean
+        if metric == "gaussian":
+            return np.sum(d * d, axis=1)
+        elif metric == "maha":
+            cholesky_factor = np.linalg.cholesky(covariance)
+            z = scipy.linalg.solve_triangular(cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True)
+            return np.sum(z * z, axis=0)  # square maha
+        else:
+            raise ValueError("Invalid distance metric")
+
+
+class KalmanFilterXYWH(KalmanFilterXYAH):
+    """
+    For BoT-SORT. A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y), width
+    w, height h, and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location (x, y, w, h) is taken as direct
+    observation of the state space (linear observation model).
+    """
+
+    def initiate(self, measurement: np.ndarray) -> tuple:
+        """
+        Create track from unassociated measurement.
+
+        Args:
+            measurement (ndarray): Bounding box coordinates (x, y, w, h) with center position (x, y), width, and height.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of
+                the new track. Unobserved velocities are initialized to 0 mean.
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[2],
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[2],
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[2],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[2],
+            10 * self._std_weight_velocity * measurement[3],
+        ]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance) -> tuple:
+        """
+        Run Kalman filter prediction step.
+
+        Args:
+            mean (ndarray): The 8 dimensional mean vector of the object state at the previous time step.
+            covariance (ndarray): The 8x8 dimensional covariance matrix of the object state at the previous time step.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
+                velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+        ]
+        std_vel = [
+            self._std_weight_velocity * mean[2],
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[2],
+            self._std_weight_velocity * mean[3],
+        ]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(mean, self._motion_mat.T)
+        covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance) -> tuple:
+        """
+        Project state distribution to measurement space.
+
+        Args:
+            mean (ndarray): The state's mean vector (8 dimensional array).
+            covariance (ndarray): The state's covariance matrix (8x8 dimensional).
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the projected mean and covariance matrix of the given state estimate.
+        """
+        std = [
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[2],
+            self._std_weight_position * mean[3],
+        ]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def multi_predict(self, mean, covariance) -> tuple:
+        """
+        Run Kalman filter prediction step (Vectorized version).
+
+        Args:
+            mean (ndarray): The Nx8 dimensional mean matrix of the object states at the previous time step.
+            covariance (ndarray): The Nx8x8 covariance matrix of the object states at the previous time step.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the mean vector and covariance matrix of the predicted state. Unobserved
+                velocities are initialized to 0 mean.
+        """
+        std_pos = [
+            self._std_weight_position * mean[:, 2],
+            self._std_weight_position * mean[:, 3],
+            self._std_weight_position * mean[:, 2],
+            self._std_weight_position * mean[:, 3],
+        ]
+        std_vel = [
+            self._std_weight_velocity * mean[:, 2],
+            self._std_weight_velocity * mean[:, 3],
+            self._std_weight_velocity * mean[:, 2],
+            self._std_weight_velocity * mean[:, 3],
+        ]
+        sqr = np.square(np.r_[std_pos, std_vel]).T
+
+        motion_cov = [np.diag(sqr[i]) for i in range(len(mean))]
+        motion_cov = np.asarray(motion_cov)
+
+        mean = np.dot(mean, self._motion_mat.T)
+        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))
+        covariance = np.dot(left, self._motion_mat.T) + motion_cov
+
+        return mean, covariance
+
+    def update(self, mean, covariance, measurement) -> tuple:
+        """
+        Run Kalman filter correction step.
+
+        Args:
+            mean (ndarray): The predicted state's mean vector (8 dimensional).
+            covariance (ndarray): The state's covariance matrix (8x8 dimensional).
+            measurement (ndarray): The 4 dimensional measurement vector (x, y, w, h), where (x, y) is the center
+                position, w the width, and h the height of the bounding box.
+
+        Returns:
+            (tuple[ndarray, ndarray]): Returns the measurement-corrected state distribution.
+        """
+        return super().update(mean, covariance, measurement)

ultralytics/trackers/utils/matching.py

@@ -0,0 +1,138 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import numpy as np
+import scipy
+from scipy.spatial.distance import cdist
+
+from ultralytics.utils.metrics import bbox_ioa, batch_probiou
+
+try:
+    import lap  # for linear_assignment
+
+    assert lap.__version__  # verify package is not directory
+except (ImportError, AssertionError, AttributeError):
+    from ultralytics.utils.checks import check_requirements
+
+    check_requirements("lapx>=0.5.2")  # update to lap package from https://github.com/rathaROG/lapx
+    import lap
+
+
+def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = True) -> tuple:
+    """
+    Perform linear assignment using scipy or lap.lapjv.
+
+    Args:
+        cost_matrix (np.ndarray): The matrix containing cost values for assignments.
+        thresh (float): Threshold for considering an assignment valid.
+        use_lap (bool, optional): Whether to use lap.lapjv. Defaults to True.
+
+    Returns:
+        Tuple with:
+            - matched indices
+            - unmatched indices from 'a'
+            - unmatched indices from 'b'
+    """
+
+    if cost_matrix.size == 0:
+        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
+
+    if use_lap:
+        # Use lap.lapjv
+        # https://github.com/gatagat/lap
+        _, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
+        matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0]
+        unmatched_a = np.where(x < 0)[0]
+        unmatched_b = np.where(y < 0)[0]
+    else:
+        # Use scipy.optimize.linear_sum_assignment
+        # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
+        x, y = scipy.optimize.linear_sum_assignment(cost_matrix)  # row x, col y
+        matches = np.asarray([[x[i], y[i]] for i in range(len(x)) if cost_matrix[x[i], y[i]] <= thresh])
+        if len(matches) == 0:
+            unmatched_a = list(np.arange(cost_matrix.shape[0]))
+            unmatched_b = list(np.arange(cost_matrix.shape[1]))
+        else:
+            unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0]))
+            unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1]))
+
+    return matches, unmatched_a, unmatched_b
+
+
+def iou_distance(atracks: list, btracks: list) -> np.ndarray:
+    """
+    Compute cost based on Intersection over Union (IoU) between tracks.
+
+    Args:
+        atracks (list[STrack] | list[np.ndarray]): List of tracks 'a' or bounding boxes.
+        btracks (list[STrack] | list[np.ndarray]): List of tracks 'b' or bounding boxes.
+
+    Returns:
+        (np.ndarray): Cost matrix computed based on IoU.
+    """
+
+    if atracks and isinstance(atracks[0], np.ndarray) or btracks and isinstance(btracks[0], np.ndarray):
+        atlbrs = atracks
+        btlbrs = btracks
+    else:
+        atlbrs = [track.xywha if track.angle is not None else track.xyxy for track in atracks]
+        btlbrs = [track.xywha if track.angle is not None else track.xyxy for track in btracks]
+
+    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
+    if len(atlbrs) and len(btlbrs):
+        if len(atlbrs[0]) == 5 and len(btlbrs[0]) == 5:
+            ious = batch_probiou(
+                np.ascontiguousarray(atlbrs, dtype=np.float32),
+                np.ascontiguousarray(btlbrs, dtype=np.float32),
+            ).numpy()
+        else:
+            ious = bbox_ioa(
+                np.ascontiguousarray(atlbrs, dtype=np.float32),
+                np.ascontiguousarray(btlbrs, dtype=np.float32),
+                iou=True,
+            )
+    return 1 - ious  # cost matrix
+
+
+def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -> np.ndarray:
+    """
+    Compute distance between tracks and detections based on embeddings.
+
+    Args:
+        tracks (list[STrack]): List of tracks.
+        detections (list[BaseTrack]): List of detections.
+        metric (str, optional): Metric for distance computation. Defaults to 'cosine'.
+
+    Returns:
+        (np.ndarray): Cost matrix computed based on embeddings.
+    """
+
+    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
+    if cost_matrix.size == 0:
+        return cost_matrix
+    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32)
+    # for i, track in enumerate(tracks):
+    # cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
+    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
+    cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Normalized features
+    return cost_matrix
+
+
+def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray:
+    """
+    Fuses cost matrix with detection scores to produce a single similarity matrix.
+
+    Args:
+        cost_matrix (np.ndarray): The matrix containing cost values for assignments.
+        detections (list[BaseTrack]): List of detections with scores.
+
+    Returns:
+        (np.ndarray): Fused similarity matrix.
+    """
+
+    if cost_matrix.size == 0:
+        return cost_matrix
+    iou_sim = 1 - cost_matrix
+    det_scores = np.array([det.score for det in detections])
+    det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
+    fuse_sim = iou_sim * det_scores
+    return 1 - fuse_sim  # fuse_cost