1.下载官方代码

mikel-brostrom/yolov8_tracking: Real-time multi-object tracking and segmentation using YOLOv8 with DeepOCSORT and OSNet (github.com)https://github.com/mikel-brostrom/yolov8_tracking

1.到上面的github网站下载跟踪代码，该代码使用YOLOv8作为检测网络，不过YOLOv8的文件需要去下面的链接下载，然后替换掉跟踪代码中YOLOv8的空文件。

GitHub – ultralytics/ultralytics at 15b3b0365ab2f12993a58985f3cb7f2137409a0cNEW – YOLOv8in PyTorch > ONNX > CoreML > TFLite – GitHub – ultralytics/ultralytics at 15b3b0365ab2f12993a58985f3cb7f2137409a0chttps://github.com/ultralytics/ultralytics/tree/15b3b0365ab2f12993a58985f3cb7f2137409a0c2.该跟踪网络可以选择自行选择BoT-sort、Bytetrack、Deepocsort、Ocsort和Strongsort作为跟踪网络。我自己使用下来感觉Deepocsort和Bytetrack效果最好。

3.里面还有一个重识别网络也叫Reid网络，是用来匹配轨迹和检测框中国物体的外观特征的。里面的权重模型在下面的链接进行下载。其中market1501是清华大学的行人数据集，我用了之后发现效果不好（可能是数据集太老的原因）。msmt17数据集效果很不错，推荐下该数据集的预训练模型。模型越大，特征提取效果越好，一般使用osnet也够用，如果想要效果更好就推荐Resnet50，或者自己用更好的图像分类网络在msmt17上预训练。Model Zoo — torchreid 1.4.0 documentationhttps://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO

2.运行代码

其实github中有给出使用的方法，我在这里就不多说了，主要讲一下需要注意的地方。

1.安装需要的包

pip install -r requirements.txt

2.需要安装一个单独的包，不然后面会报错

pip install lap

3.将download.py文件第156行的verify改为False

不然代码会自动访问google的库下载权重文件

4.修改yolov8_tracking-master/trackers/strongsort/deep/models

/osnet.py中第483行的权重文件地址。

要先把reid网络的权重文件下载下来，修改成对应地址

5.将MOT数据集中的图片合成为视频，再进行检测

因为MOT数据集中全是一帧一帧的图片，所以我们先要将它整合成视频。代码为

import osimport cv2video_writer = cv2.VideoWriter("result.mp4", cv2.VideoWriter_fourcc(*"mp4v"), 30, (1920, 1080))images_path = "..."images_list = os.listdir(images_path)images_list.sort()for image_name in images_list:image = cv2.imread(os.path.join(images_path, image_name))video_writer.write(image)show = cv2.resize(image, (1280, 720))cv2.imshow("test", show)if cv2.waitKey(10) != ord('q'):pass

6.最后在终端输入命令就可以运行起来了

7.Deepocsort代码讲解

因为我一开始完全看不懂代码，自己摸索真的很累，所以我想能够帮助需要的人来快速理解代码，重要部分我都已经打上注释，还有不理解的地方可以在评论区留言。

"""This script is adopted from the SORT script by Alex Bewley alex@bewley.ai"""from __future__ import print_functionimport pdbimport pickleimport cv2import torchimport torchvisionimport numpy as npfrom .association import *from .embedding import EmbeddingComputerfrom .cmc import CMCComputerfrom reid_multibackend import ReIDDetectMultiBackenddef k_previous_obs(observations, cur_age, k):if len(observations) == 0:# 若轨迹的观测数为0return [-1, -1, -1, -1, -1]for i in range(k):dt = k - iif cur_age - dt in observations:return observations[cur_age - dt]max_age = max(observations.keys())return observations[max_age]def convert_bbox_to_z(bbox):"""Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r isthe aspect ratio"""w = bbox[2] - bbox[0]h = bbox[3] - bbox[1]x = bbox[0] + w / 2.0y = bbox[1] + h / 2.0s = w * h# scale is just arear = w / float(h + 1e-6)return np.array([x, y, s, r]).reshape((4, 1))def convert_bbox_to_z_new(bbox):w = bbox[2] - bbox[0]h = bbox[3] - bbox[1]x = bbox[0] + w / 2.0y = bbox[1] + h / 2.0return np.array([x, y, w, h]).reshape((4, 1))def convert_x_to_bbox_new(x):x, y, w, h = x.reshape(-1)[:4]return np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2]).reshape(1, 4)def convert_x_to_bbox(x, score=None):"""Takes a bounding box in the centre form [x,y,s,r] and returns it in the form[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right"""w = np.sqrt(x[2] * x[3])h = x[2] / wif score == None:return np.array([x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0]).reshape((1, 4))else:return np.array([x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0, score]).reshape((1, 5))def speed_direction(bbox1, bbox2):cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0speed = np.array([cy2 - cy1, cx2 - cx1])norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6return speed / normdef new_kf_process_noise(w, h, p=1 / 20, v=1 / 160):Q = np.diag(((p * w) ** 2, (p * h) ** 2, (p * w) ** 2, (p * h) ** 2, (v * w) ** 2, (v * h) ** 2, (v * w) ** 2, (v * h) ** 2))return Qdef new_kf_measurement_noise(w, h, m=1 / 20):w_var = (m * w) ** 2h_var = (m * h) ** 2R = np.diag((w_var, h_var, w_var, h_var))return Rclass KalmanBoxTracker(object):"""This class represents the internal state of individual tracked objects observed as bbox."""count = 0def __init__(self, bbox, cls, delta_t=3, orig=False, emb=None, alpha=0, new_kf=False):"""Initialises a tracker using initial bounding box."""# define constant velocity modelif not orig:from .kalmanfilter import KalmanFilterNew as KalmanFilterelse:from filterpy.kalman import KalmanFilterself.cls = clsself.conf = bbox[-1]self.new_kf = new_kfif new_kf:self.kf = KalmanFilter(dim_x=8, dim_z=4)self.kf.F = np.array([# x y w h x' y' w' h'[1, 0, 0, 0, 1, 0, 0, 0],[0, 1, 0, 0, 0, 1, 0, 0],[0, 0, 1, 0, 0, 0, 1, 0],[0, 0, 0, 1, 0, 0, 0, 1],[0, 0, 0, 0, 1, 0, 0, 0],[0, 0, 0, 0, 0, 1, 0, 0],[0, 0, 0, 0, 0, 0, 1, 0],[0, 0, 0, 0, 0, 0, 0, 1],])self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0, 0],[0, 1, 0, 0, 0, 0, 0, 0],[0, 0, 1, 0, 0, 0, 0, 0],[0, 0, 0, 1, 0, 0, 0, 0],])_, _, w, h = convert_bbox_to_z_new(bbox).reshape(-1)self.kf.P = new_kf_process_noise(w, h)self.kf.P[:4, :4] *= 4self.kf.P[4:, 4:] *= 100# Process and measurement uncertainty happen in functionsself.bbox_to_z_func = convert_bbox_to_z_newself.x_to_bbox_func = convert_x_to_bbox_newelse:self.kf = KalmanFilter(dim_x=7, dim_z=4)self.kf.F = np.array([# xysrx' y' s'[1, 0, 0, 0, 1, 0, 0],[0, 1, 0, 0, 0, 1, 0],[0, 0, 1, 0, 0, 0, 1],[0, 0, 0, 1, 0, 0, 0],[0, 0, 0, 0, 1, 0, 0],[0, 0, 0, 0, 0, 1, 0],[0, 0, 0, 0, 0, 0, 1],])self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0],[0, 1, 0, 0, 0, 0, 0],[0, 0, 1, 0, 0, 0, 0],[0, 0, 0, 1, 0, 0, 0],])self.kf.R[2:, 2:] *= 10.0self.kf.P[4:, 4:] *= 1000.0# give high uncertainty to the unobservable initial velocitiesself.kf.P *= 10.0self.kf.Q[-1, -1] *= 0.01self.kf.Q[4:, 4:] *= 0.01self.bbox_to_z_func = convert_bbox_to_zself.x_to_bbox_func = convert_x_to_bboxself.kf.x[:4] = self.bbox_to_z_func(bbox)self.time_since_update = 0# 每有一个卡尔曼轨迹id就加一self.id = KalmanBoxTracker.countKalmanBoxTracker.count += 1self.history = []self.hits = 0self.hit_streak = 0self.age = 0"""NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now."""# Used for OCRself.last_observation = np.array([-1, -1, -1, -1, -1])# placeholder# Used to output track after min_hits reachedself.history_observations = []# Used for velocityself.observations = dict()self.velocity = Noneself.delta_t = delta_tself.emb = embself.frozen = Falsedef update(self, bbox, cls):"""Updates the state vector with observed bbox."""if bbox is not None:self.frozen = Falseself.cls = clsif self.last_observation.sum() >= 0:# no previous observationprevious_box = Nonefor dt in range(self.delta_t, 0, -1):if self.age - dt in self.observations:previous_box = self.observations[self.age - dt]breakif previous_box is None:previous_box = self.last_observation"""Estimate the track speed direction with observations \Delta t steps away"""self.velocity = speed_direction(previous_box, bbox)"""Insert new observations. This is a ugly way to maintain both self.observationsand self.history_observations. Bear it for the moment."""self.last_observation = bboxself.observations[self.age] = bboxself.history_observations.append(bbox)self.time_since_update = 0self.history = []self.hits += 1self.hit_streak += 1if self.new_kf:R = new_kf_measurement_noise(self.kf.x[2, 0], self.kf.x[3, 0])self.kf.update(self.bbox_to_z_func(bbox), R=R)else:self.kf.update(self.bbox_to_z_func(bbox))else:self.kf.update(bbox)self.frozen = Truedef update_emb(self, emb, alpha=0.9):self.emb = alpha * self.emb + (1 - alpha) * embself.emb /= np.linalg.norm(self.emb)def get_emb(self):return self.emb.cpu()def apply_affine_correction(self, affine):m = affine[:, :2]t = affine[:, 2].reshape(2, 1)# For OCRif self.last_observation.sum() > 0:ps = self.last_observation[:4].reshape(2, 2).Tps = m @ ps + tself.last_observation[:4] = ps.T.reshape(-1)# Apply to each box in the range of velocity computationfor dt in range(self.delta_t, -1, -1):if self.age - dt in self.observations:ps = self.observations[self.age - dt][:4].reshape(2, 2).Tps = m @ ps + tself.observations[self.age - dt][:4] = ps.T.reshape(-1)# Also need to change kf state, but might be frozenself.kf.apply_affine_correction(m, t, self.new_kf)def predict(self):"""Advances the state vector and returns the predicted bounding box estimate."""# Don't allow negative bounding boxesif self.new_kf:if self.kf.x[2] + self.kf.x[6] <= 0:self.kf.x[6] = 0if self.kf.x[3] + self.kf.x[7] <= 0:self.kf.x[7] = 0# Stop velocity, will update in kf during OOSif self.frozen:self.kf.x[6] = self.kf.x[7] = 0Q = new_kf_process_noise(self.kf.x[2, 0], self.kf.x[3, 0])else:if (self.kf.x[6] + self.kf.x[2])  0:self.hit_streak = 0self.time_since_update += 1self.history.append(self.x_to_bbox_func(self.kf.x))return self.history[-1]def get_state(self):"""Returns the current bounding box estimate."""return self.x_to_bbox_func(self.kf.x)def mahalanobis(self, bbox):"""Should be run after a predict() call for accuracy."""return self.kf.md_for_measurement(self.bbox_to_z_func(bbox))"""We support multiple ways for association cost calculation, by defaultwe use IoU. GIoU may have better performance in some situations. We note that we hardly normalize the cost by all methods to (0,1) which may not be the best practice."""ASSO_FUNCS = {"iou": iou_batch,"giou": giou_batch,"ciou": ciou_batch,"diou": diou_batch,"ct_dist": ct_dist,}class OCSort(object):def __init__(self,# reid模型的权重文件model_weights,device,# 是否需要半精度fp16,# 检测框的置信度阈值det_thresh,# 轨迹的最大生命，轨迹每预测一次，# trk.time_since_update就加一，知道大于max_age后被删除max_age=30,# 轨道的最小连击值，卡尔曼滤波每更新一次，hit_streak就加一min_hits=3,# 检测框和轨迹之间IOU的最小阈值iou_threshold=0.3,delta_t=3,# 检测框和轨迹之间计算IOU的方式asso_func="iou",# vdc_weight权重inertia=0.2,w_association_emb=0.75,alpha_fixed_emb=0.95,aw_param=0.5,embedding_off=False,cmc_off=False,aw_off=False,new_kf_off=True,**kwargs):"""Sets key parameters for SORT"""self.max_age = max_ageself.min_hits = min_hitsself.iou_threshold = iou_thresholdself.trackers = []self.frame_count = 0# 检测框的置信度阈值self.det_thresh = det_threshself.delta_t = delta_tself.asso_func = ASSO_FUNCS[asso_func]self.inertia = inertiaself.w_association_emb = w_association_embself.alpha_fixed_emb = alpha_fixed_embself.aw_param = aw_param# 初始化id为0KalmanBoxTracker.count = 0# embedder为reid提取出来的特征self.embedder = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16)# CMC为累积匹配特性，用于评价reid的self.cmc = CMCComputer()self.embedding_off = embedding_offself.cmc_off = cmc_offself.aw_off = aw_offself.new_kf_off = new_kf_offself.s = 0def update(self, dets, img_numpy, tag='blub'):"""Params:dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).Returns the a similar array, where the last column is the object ID.NOTE: The number of objects returned may differ from the number of detections provided."""xyxys = dets[:, 0:4]scores = dets[:, 4]clss = dets[:, 5]classes = clss.numpy()xyxys = xyxys.numpy()scores = scores.numpy()dets = dets[:, 0:6].numpy()# 当检测框的置信度大于阈值时remain_inds = scores > self.det_thresh# 检测框dets为大于阈值的检测框dets = dets[remain_inds]self.height, self.width = img_numpy.shape[:2]# Rescale#scale = min(img_tensor.shape[2] / img_numpy.shape[0], img_tensor.shape[3] / img_numpy.shape[1])#dets[:, :4] /= scale# Embeddingif self.embedding_off or dets.shape[0] == 0:# 如果不提取特征或没有超过阈值的检测框dets_embs = np.ones((dets.shape[0], 1))else:# (Ndets x X) [512, 1024, 2048]#dets_embs = self.embedder.compute_embedding(img_numpy, dets[:, :4], tag)# 输入为检测出来的框和原图的numpy# 输出为检测框对应原图的特征dets_embs = self._get_features(dets[:, :4], img_numpy)# CMC# CMC用来评估匹配成功的概率if not self.cmc_off:transform = self.cmc.compute_affine(img_numpy, dets[:, :4], tag)for trk in self.trackers:trk.apply_affine_correction(transform)# (检测框的score-检测框置信度阈值)/(1-检测框置信度阈值)trust = (dets[:, 4] - self.det_thresh) / (1 - self.det_thresh)af = self.alpha_fixed_emb# From [self.alpha_fixed_emb, 1], goes to 1 as detector is less confidentdets_alpha = af + (1 - af) * (1 - trust)# get predicted locations from existing trackers.# 从现存的轨迹预测位置# 生成一个trackers行，5列的数组trks = np.zeros((len(self.trackers), 5))trk_embs = []to_del = []ret = []for t, trk in enumerate(trks):# 用轨迹去预测位置pos = self.trackers[t].predict()[0]# 用预测的位置放入生成的轨迹trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]# np.any()对矩阵所有元素做或运算，存在True则返回True# np.isnan(x)函数可以判断x是否为空值，然后输出布尔类型的变量if np.any(np.isnan(pos)):# 若预测的位置不存在，则添加到to_del中to_del.append(t)else:# 存在则将该轨迹的特征添加到轨迹特征列表中trk_embs.append(self.trackers[t].get_emb())trks = np.ma.compress_rows(np.ma.masked_invalid(trks))if len(trk_embs) > 0:# np.vstack():在竖直方向上堆叠# np.hstack():在水平方向上平铺# np.vstack()输入为元组，返回堆叠后的新数组trk_embs = np.vstack(trk_embs)else:trk_embs = np.array(trk_embs)for t in reversed(to_del):# 从轨迹中删除预测的位置为空的轨迹self.trackers.pop(t)# 如果轨迹的速度不是None，velocities为轨迹的速度velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])# 最新的框为轨迹的last_observationlast_boxes = np.array([trk.last_observation for trk in self.trackers])k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])"""First round of association"""# (M detections X N tracks, final score)if self.embedding_off or dets.shape[0] == 0 or trk_embs.shape[0] == 0:stage1_emb_cost = Noneelse:# @表示矩阵乘法# 检测框的特征乘轨迹的特征得到代价矩阵stage1_emb_cost = dets_embs @ trk_embs.T# 第一次匹配matched, unmatched_dets, unmatched_trks = associate(dets,trks,self.iou_threshold,velocities,k_observations,self.inertia,stage1_emb_cost,self.w_association_emb,self.aw_off,self.aw_param,)# m0是检测框，m1是轨迹for m in matched:# 更新该轨迹的检测框self.trackers[m[1]].update(dets[m[0], :5], dets[m[0], 5])# 更新该轨迹的人物特征self.trackers[m[1]].update_emb(dets_embs[m[0]], alpha=dets_alpha[m[0]])"""Second round of associaton by OCR"""# 第二次匹配# 如果还有剩下的检测框和轨迹没有匹配的，进行第二次匹配if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:left_dets = dets[unmatched_dets]left_dets_embs = dets_embs[unmatched_dets]left_trks = last_boxes[unmatched_trks]left_trks_embs = trk_embs[unmatched_trks]# print(dets.shape) # (9, 6)# print(left_dets.shape) # (1, 6)# print(left_trks.shape) # (1, 5)iou_left = self.asso_func(left_dets, left_trks)# TODO: is better without thisemb_cost_left = left_dets_embs @ left_trks_embs.Tif self.embedding_off:emb_cost_left = np.zeros_like(emb_cost_left)iou_left = np.array(iou_left)# 如果留下的检测框和轨迹的iou大于阈值if iou_left.max() > self.iou_threshold:"""NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you mayget a higher performance especially on MOT17/MOT20 datasets. But we keep ituniform here for simplicity"""rematched_indices = linear_assignment(-iou_left)to_remove_det_indices = []to_remove_trk_indices = []for m in rematched_indices:det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]if iou_left[m[0], m[1]]  150:self.trackers[m].update(None, None)# create and initialise new trackers for unmatched detections# 遍历匹配失败的检测框，生成新的轨迹for i in unmatched_dets:trk = KalmanBoxTracker(dets[i, :5], dets[i, 5], delta_t=self.delta_t, emb=dets_embs[i], alpha=dets_alpha[i], new_kf=not self.new_kf_off)self.trackers.append(trk)i = len(self.trackers)# 遍历轨迹的逆序序列for trk in reversed(self.trackers):if trk.last_observation.sum() < 0:d = trk.get_state()[0]else:"""this is optional to use the recent observation or the kalman filter prediction,we didn't notice significant difference here"""d = trk.last_observation[:4]if (trk.time_since_update = self.min_hits or self.frame_count  self.max_age:self.trackers.pop(i)if len(ret) > 0:return np.concatenate(ret)return np.empty((0, 5))def _xywh_to_xyxy(self, bbox_xywh):x, y, w, h = bbox_xywhx1 = max(int(x - w / 2), 0)x2 = min(int(x + w / 2), self.width - 1)y1 = max(int(y - h / 2), 0)y2 = min(int(y + h / 2), self.height - 1)return x1, y1, x2, y2def _get_features(self, bbox_xywh, ori_img):im_crops = []for box in bbox_xywh:x1, y1, x2, y2 = self._xywh_to_xyxy(box)im = ori_img[y1:y2, x1:x2]im_crops.append(im)if im_crops:features = self.embedder(im_crops).cpu()else:features = np.array([])return featuresdef update_public(self, dets, cates, scores):self.frame_count += 1det_scores = np.ones((dets.shape[0], 1))dets = np.concatenate((dets, det_scores), axis=1)remain_inds = scores > self.det_threshcates = cates[remain_inds]dets = dets[remain_inds]trks = np.zeros((len(self.trackers), 5))to_del = []ret = []for t, trk in enumerate(trks):pos = self.trackers[t].predict()[0]cat = self.trackers[t].catetrk[:] = [pos[0], pos[1], pos[2], pos[3], cat]if np.any(np.isnan(pos)):to_del.append(t)trks = np.ma.compress_rows(np.ma.masked_invalid(trks))for t in reversed(to_del):self.trackers.pop(t)velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])last_boxes = np.array([trk.last_observation for trk in self.trackers])k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])matched, unmatched_dets, unmatched_trks = associate_kitti(dets,trks,cates,self.iou_threshold,velocities,k_observations,self.inertia,)for m in matched:self.trackers[m[1]].update(dets[m[0], :])if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:"""The re-association stage by OCR.NOTE: at this stage, adding other strategy might be able to continue improvethe performance, such as BYTE association by ByteTrack."""left_dets = dets[unmatched_dets]left_trks = last_boxes[unmatched_trks]left_dets_c = left_dets.copy()left_trks_c = left_trks.copy()iou_left = self.asso_func(left_dets_c, left_trks_c)iou_left = np.array(iou_left)det_cates_left = cates[unmatched_dets]trk_cates_left = trks[unmatched_trks][:, 4]num_dets = unmatched_dets.shape[0]num_trks = unmatched_trks.shape[0]cate_matrix = np.zeros((num_dets, num_trks))for i in range(num_dets):for j in range(num_trks):if det_cates_left[i] != trk_cates_left[j]:"""For some datasets, such as KITTI, there are different categories,we have to avoid associate them together."""cate_matrix[i][j] = -1e6iou_left = iou_left + cate_matrixif iou_left.max() > self.iou_threshold - 0.1:rematched_indices = linear_assignment(-iou_left)to_remove_det_indices = []to_remove_trk_indices = []for m in rematched_indices:det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]if iou_left[m[0], m[1]]  0:d = trk.last_observation[:4]else:d = trk.get_state()[0]if trk.time_since_update < 1:if (self.frame_count = self.min_hits):# id+1 as MOT benchmark requires positiveret.append(np.concatenate((d, [trk.id + 1], [trk.cls], [trk.conf])).reshape(1, -1))if trk.hit_streak == self.min_hits:# Head Padding (HP): recover the lost steps during initializing the trackfor prev_i in range(self.min_hits - 1):prev_observation = trk.history_observations[-(prev_i + 2)]ret.append((np.concatenate((prev_observation[:4],[trk.id + 1],[trk.cls],[trk.conf],))).reshape(1, -1))i -= 1if trk.time_since_update > self.max_age:self.trackers.pop(i)if len(ret) > 0:return np.concatenate(ret)return np.empty((0, 7))def dump_cache(self):self.cmc.dump_cache()self.embedder.dump_cache()