目标检测：目标检测是指在图像或视频帧中识别和定位特定目标，并使用边界框来确定它们的位置。YOLO（You Only Look Once）是一种高效的单阶段目标检测算法，以其快速的处理速度和较高的准确性而闻名。与传统的两阶段检测算法相比，YOLO的优势在于它能够一次性处理整个图像，从而实现实时目标检测，这在自动驾驶、视频监控和机器人导航等应用中尤为重要。
目标跟踪：目标跟踪则关注于在视频序列中连续跟踪已识别的目标。SORT（Simple Online and Realtime Tracking）算法因其简单性和实时性而被广泛应用于目标跟踪任务。其通过预测目标的运动轨迹并实时更新其位置，有效实现了目标的连续跟踪。结合YOLO进行检测和SORT进行跟踪，可以实现目标的连续监控和分析，确保在整个视频序列中的准确和一致的跟踪。项目 我们将使用YOLOv8m（中等版本）、OpenCV和SORT进行目标检测，以确保准确性和效率，来计算通过我们视频中特定区域的车辆数量。
本项目旨在通过结合使用YOLOv8m（一种中等复杂度的YOLO变体）、OpenCV（一个开源的计算机视觉库）和SORT算法，实现对视频中特定区域内通过的车辆数量的准确计算。这一过程不仅确保了目标检测的准确性，也提高了整个系统的效率。
 video = cv2.VideoCapture('traffic.mp4')width = 1280height = 720line_left_road_x1 = 256line_left_road_x2 = 500line_left_road_y = 472line_right_road_x1 = 672line_right_road_x2 = 904line_right_road_y = 472vehicle_left_road_id_count = []vehicle_right_road_id_count = []while True: success, frame = video.read() if not success: break frame = cv2.resize(frame, (width, height)) image_region = cv2.bitwise_and(frame, mask) results = model(image_region, stream=True) detections = [] cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 0, 255)) cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 0, 255)) for result in results: for box in result.boxes: class_name = class_names[int(box.cls[0])] if not class_name in class_names_goal: continue confidence = round(float(box.conf[0]) * 100, 2) if confidence < 30: continue x1, y1, x2, y2 = box.xyxy[0] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) detections.append([x1, y1, x2, y2, float(box.conf[0])]) tracked_objects = tracker.update(np.array(detections)) for obj in tracked_objects: x1, y1, x2, y2, obj_id = [int(i) for i in obj] confidence_pos_x1 = max(0, x1) confidence_pos_y1 = max(36, y1) cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 2) cvzone.putTextRect(frame, f'ID: {obj_id}', (confidence_pos_x1, confidence_pos_y1), 1, 1) center_x = (x1 + x2) // 2 center_y = (y1 + y2) // 2 if line_left_road_y - 10 < center_y < line_left_road_y + 10 and line_left_road_x1 < center_x < line_left_road_x2: if not obj_id in vehicle_left_road_id_count: vehicle_left_road_id_count.append(obj_id) cv2.line(frame, (line_left_road_x1, line_left_road_y) ,(line_left_road_x2, line_left_road_y), (0, 255, 0), 2) if line_right_road_y - 10 < center_y < line_right_road_y + 10 and line_right_road_x1 < center_x < line_right_road_x2: if not obj_id in vehicle_right_road_id_count: vehicle_right_road_id_count.append(obj_id) cv2.line(frame, (line_right_road_x1, line_right_road_y) ,(line_right_road_x2, line_right_road_y), (0, 255, 0), 2) cvzone.putTextRect(frame, f'Car Left Road Count: {len(vehicle_left_road_id_count)}', (50, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31)) cvzone.putTextRect(frame, f'Car Right Road Count: {len(vehicle_right_road_id_count)}', (width - 460, 50), 2, 2, offset=20, border=2, colorR=(140, 57, 31), colorB=(140, 57, 31)) cv2.imshow('Image', frame) if cv2.waitKey(1) & 0xFF == ord('q'): breakvideo.release()cv2.destroyAllWindows()</code></pre><h4>6. 结果</h4><p style=)
源码：https://github.com/VladeMelo/collaborative-filtering
本文链接：http://www.28at.com/showinfo-26-112706-0.html一个简单的车辆目标检测和跟踪示例
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