【图像算法 - 13】基于 YOLO12 与 OpenCV 的实时目标点击跟踪系统（系统介绍 + 源码详细）

基于 YOLO12 与 OpenCV 的实时点击目标跟踪系统

在计算机视觉领域，目标检测与跟踪是两个核心任务。本文将介绍一个结合 YOLO 目标检测模型与 OpenCV 跟踪算法的实时目标跟踪系统，该系统允许用户通过鼠标交互选择特定目标进行持续跟踪，支持多种跟踪算法切换，适用于视频监控、行为分析等场景。

【图像算法 – 13】基于 YOLO12 与 OpenCV 的实时目标点击跟踪系统

系统功能概述

该系统主要实现以下功能：

• 使用 YOLO 模型对视频帧进行目标检测，识别出画面中的各类物体

• 支持用户通过左键点击选择特定目标进行跟踪

• 提供 MOSSE、CSRT、KCF 三种经典跟踪算法供选择

• 实时显示跟踪状态、目标类别及 FPS 等信息

• 支持右键点击停止跟踪，回到目标检测模式

技术原理

系统采用 “检测 + 跟踪” 的混合架构：

这种架构结合了 YOLO 检测精度高和传统跟踪算法速度快的优点，在保证一定精度的同时兼顾了实时性。

代码解析

核心依赖库

import cv2 # 用于视频处理和跟踪算法
import numpy as np # 用于数值计算
import argparse # 用于命令行参数解析
from ultralytics import YOLO # 用于YOLO目标检测

全局变量与交互设计

定义全局变量存储跟踪状态和用户交互信息：

selected_box = None # 选中的目标边界框
tracking = False # 跟踪状态标志
target_class = None # 目标类别
click_x, click_y = –1, –1 # 鼠标点击坐标
mouse_clicked = False # 鼠标点击标志
debug = False # 调试模式标志

鼠标回调函数处理用户交互：

def mouse_callback(event, x, y, flags, param):
global click_x, click_y, mouse_clicked, tracking
if event == cv2.EVENT_LBUTTONDOWN: # 左键点击选择目标
click_x, click_y = x, y
mouse_clicked = True
if debug:
print(f"Left click at: ({x}, {y})")
elif event == cv2.EVENT_RBUTTONDOWN: # 右键点击停止跟踪
tracking = False
if debug:
print("Tracking stopped")

跟踪器创建

针对不同的跟踪算法，创建对应的跟踪器实例（注意 OpenCV 新版本中跟踪器位于 legacy 模块）：

def create_tracker(tracker_type):
"""使用cv2.legacy模块创建跟踪器，兼容新版OpenCV"""
try:
if tracker_type == "mosse":
return cv2.legacy.TrackerMOSSE_create()
elif tracker_type == "csrt":
return cv2.legacy.TrackerCSRT_create()
elif tracker_type == "kcf":
return cv2.legacy.TrackerKCF_create()
else:
print(f"Unsupported tracker: {tracker_type}, using MOSSE")
return cv2.legacy.TrackerMOSSE_create()
except AttributeError as e:
print(f"Failed to create tracker: {e}")
print("Please check OpenCV installation (must include opencv-contrib-python)")
return None

OpenCV 跟踪demo源码

#!/usr/bin/env python
'''
Tracker demo

For usage download models by following links
For GOTURN:
goturn.prototxt and goturn.caffemodel: https://github.com/opencv/opencv_extra/tree/c4219d5eb3105ed8e634278fad312a1a8d2c182d/testdata/tracking
For DaSiamRPN:
network: https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0
kernel_r1: https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0
kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0
For NanoTrack:
nanotrack_backbone: https://github.com/HonglinChu/SiamTrackers/blob/master/NanoTrack/models/nanotrackv2/nanotrack_backbone_sim.onnx
nanotrack_headneck: https://github.com/HonglinChu/SiamTrackers/blob/master/NanoTrack/models/nanotrackv2/nanotrack_head_sim.onnx

USAGE:
tracker.py [-h] [–input INPUT_VIDEO]
[–tracker_algo TRACKER_ALGO (mil, goturn, dasiamrpn, nanotrack, vittrack)]
[–goturn GOTURN_PROTOTXT]
[–goturn_model GOTURN_MODEL]
[–dasiamrpn_net DASIAMRPN_NET]
[–dasiamrpn_kernel_r1 DASIAMRPN_KERNEL_R1]
[–dasiamrpn_kernel_cls1 DASIAMRPN_KERNEL_CLS1]
[–nanotrack_backbone NANOTRACK_BACKBONE]
[–nanotrack_headneck NANOTRACK_TARGET]
[–vittrack_net VITTRACK_MODEL]
[–vittrack_net VITTRACK_MODEL]
[–tracking_score_threshold TRACKING SCORE THRESHOLD FOR ONLY VITTRACK]
[–backend CHOOSE ONE OF COMPUTATION BACKEND]
[–target CHOOSE ONE OF COMPUTATION TARGET]
'''

# Python 2/3 compatibility
from __future__ import print_function

import sys

import numpy as np
import cv2 as cv
import argparse

from video import create_capture, presets

backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)

class App(object):

def __init__(self, args):
self.args = args
self.trackerAlgorithm = args.tracker_algo
self.tracker = self.createTracker()

def createTracker(self):
if self.trackerAlgorithm == 'mil':
tracker = cv.TrackerMIL_create()
elif self.trackerAlgorithm == 'goturn':
params = cv.TrackerGOTURN_Params()
params.modelTxt = self.args.goturn
params.modelBin = self.args.goturn_model
tracker = cv.TrackerGOTURN_create(params)
elif self.trackerAlgorithm == 'dasiamrpn':
params = cv.TrackerDaSiamRPN_Params()
params.model = self.args.dasiamrpn_net
params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1
params.kernel_r1 = self.args.dasiamrpn_kernel_r1
params.backend = args.backend
params.target = args.target
tracker = cv.TrackerDaSiamRPN_create(params)
elif self.trackerAlgorithm == 'nanotrack':
params = cv.TrackerNano_Params()
params.backbone = args.nanotrack_backbone
params.neckhead = args.nanotrack_headneck
params.backend = args.backend
params.target = args.target
tracker = cv.TrackerNano_create(params)
elif self.trackerAlgorithm == 'vittrack':
params = cv.TrackerVit_Params()
params.net = args.vittrack_net
params.tracking_score_threshold = args.tracking_score_threshold
params.backend = args.backend
params.target = args.target
tracker = cv.TrackerVit_create(params)
else:
sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn, nanotrack.".format(self.trackerAlgorithm))
return tracker

def initializeTracker(self, image):
while True:
print('==> Select object ROI for tracker …')
bbox = cv.selectROI('tracking', image)
print('ROI: {}'.format(bbox))
if bbox[2] <= 0 or bbox[3] <= 0:
sys.exit("ROI selection cancelled. Exiting…")

try:
self.tracker.init(image, bbox)
except Exception as e:
print('Unable to initialize tracker with requested bounding box. Is there any object?')
print(e)
print('Try again …')
continue

return

def run(self):
videoPath = self.args.input
print('Using video: {}'.format(videoPath))
camera = create_capture(cv.samples.findFileOrKeep(videoPath), presets['cube'])
if not camera.isOpened():
sys.exit("Can't open video stream: {}".format(videoPath))

ok, image = camera.read()
if not ok:
sys.exit("Can't read first frame")
assert image is not None

cv.namedWindow('tracking')
self.initializeTracker(image)

print("==> Tracking is started. Press 'SPACE' to re-initialize tracker or 'ESC' for exit…")

while camera.isOpened():
ok, image = camera.read()
if not ok:
print("Can't read frame")
break

ok, newbox = self.tracker.update(image)
#print(ok, newbox)

if ok:
cv.rectangle(image, newbox, (200,0,0))

cv.imshow("tracking", image)
k = cv.waitKey(1)
if k == 32: # SPACE
self.initializeTracker(image)
if k == 27: # ESC
break

print('Done')

if __name__ == '__main__':
print(__doc__)
parser = argparse.ArgumentParser(description="Run tracker")
parser.add_argument("–input", type=str, default="vtest.avi", help="Path to video source")
parser.add_argument("–tracker_algo", type=str, default="nanotrack", help="One of available tracking algorithms: mil, goturn, dasiamrpn, nanotrack, vittrack")
parser.add_argument("–goturn", type=str, default="goturn.prototxt", help="Path to GOTURN architecture")
parser.add_argument("–goturn_model", type=str, default="goturn.caffemodel", help="Path to GOTERN model")
parser.add_argument("–dasiamrpn_net", type=str, default="dasiamrpn_model.onnx", help="Path to onnx model of DaSiamRPN net")
parser.add_argument("–dasiamrpn_kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Path to onnx model of DaSiamRPN kernel_r1")
parser.add_argument("–dasiamrpn_kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Path to onnx model of DaSiamRPN kernel_cls1")
parser.add_argument("–nanotrack_backbone", type=str, default="nanotrack_backbone_sim.onnx", help="Path to onnx model of NanoTrack backBone")
parser.add_argument("–nanotrack_headneck", type=str, default="nanotrack_head_sim.onnx", help="Path to onnx model of NanoTrack headNeck")
parser.add_argument("–vittrack_net", type=str, default="vitTracker.onnx", help="Path to onnx model of vittrack")
parser.add_argument('–tracking_score_threshold', type=float, help="Tracking score threshold. If a bbox of score >= 0.3, it is considered as found ")
parser.add_argument('–backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA"% backends)
parser.add_argument("–target", choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help="Choose one of target computation devices: "
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU, '
'%d: VULKAN, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)'% targets)

args = parser.parse_args()
App(args).run()
cv.destroyAllWindows()

目标检测与处理

使用 YOLO 模型进行目标检测，并将结果转换为便于处理的格式：

def detect_objects(frame, model, confidence):
results = model(frame, conf=confidence)
boxes = [] # 存储边界框 (x, y, w, h)
class_names = [] # 存储目标类别
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist()
boxes.append((int(x1), int(y1), int(x2 – x1), int(y2 – y1)))
class_names.append(model.names[int(box.cls[0])])
return boxes, class_names

判断鼠标点击是否在某个目标框内：

def point_in_box(x, y, box):
bx, by, bw, bh = box
return bx <= x <= bx + bw and by <= y <= by + bh

主程序逻辑

主函数协调整个系统的工作流程：

def main():
global selected_box, tracking, target_class, mouse_clicked, debug

args = parse_args()
debug = args.debug

# 验证跟踪器是否可用
test_tracker = create_tracker(args.tracker)
if test_tracker is None:
return
del test_tracker

# 加载YOLO模型
try:
model = YOLO(args.model)
except Exception as e:
print(f"Failed to load YOLO model: {e}")
return

# 打开视频源
cap = cv2.VideoCapture(args.video)
if not cap.isOpened():
print(f"Cannot open video source: {args.video}")
return

# 创建窗口和鼠标回调
window_name = "Tracker"
cv2.namedWindow(window_name)
cv2.setMouseCallback(window_name, mouse_callback)

tracker = None
font = cv2.FONT_HERSHEY_SIMPLEX

while True:
ret, frame = cap.read()
if not ret:
print("End of video")
break

# 检测目标
boxes, class_names = detect_objects(frame, model, args.confidence)

# 处理点击选择目标
if mouse_clicked and not tracking:
for i, (box, cls) in enumerate(zip(boxes, class_names)):
if point_in_box(click_x, click_y, box):
selected_box = box
target_class = cls
tracker = create_tracker(args.tracker)
if tracker:
tracker.init(frame, selected_box)
tracking = True
print(f"Tracking {target_class} with {args.tracker}")
break
mouse_clicked = False

# 跟踪逻辑
if tracking and tracker:
ok, bbox = tracker.update(frame)
if ok:
x, y, w, h = [int(v) for v in bbox]
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(frame, f"Track: {target_class}", (x, y – 10),
font, 0.5, (0, 255, 0), 2)
else:
cv2.putText(frame, "Lost", (10, 30), font, 0.7, (0, 0, 255), 2)
tracking = False

# 未跟踪时显示所有目标
if not tracking:
for box, cls in zip(boxes, class_names):
x, y, w, h = box
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(frame, cls, (x, y – 10), font, 0.5, (255, 0, 0), 2)

# 显示提示信息
hint = "Right click to stop" if tracking else "Left click to select"
cv2.putText(frame, hint, (10, 30), font, 0.7, (0, 255, 255), 2)
cv2.putText(frame, f"Tracker: {args.tracker}", (10, frame.shape[0] – 30),
font, 0.5, (255, 255, 0), 2)
cv2.putText(frame, f"FPS: {int(cap.get(cv2.CAP_PROP_FPS))}",
(10, frame.shape[0] – 10), font, 0.5, (255, 255, 0), 2)

# 显示窗口
cv2.imshow(window_name, frame)

# 退出条件
if cv2.waitKey(1) & 0xFF == ord('q'):
break

cap.release()
cv2.destroyAllWindows()

使用方法

环境配置

首先安装必要的依赖库：

pip install opencv-python opencv-contrib-python ultralytics numpy

运行参数

程序支持以下命令行参数：

• –video：视频源路径，默认为 “xxxxx.mp4”，使用 “0” 可调用摄像头
• –confidence：目标检测置信度阈值，默认 0.5
• –model：YOLO 模型路径，默认 “yolo12n.pt”（会自动下载）
• –tracker：跟踪算法选择，可选 “mosse”、“csrt”、“kcf”，默认 “mosse”
• –debug：启用调试模式，打印额外信息

操作指南

算法特性对比

三种跟踪算法各有特点：

• MOSSE：速度最快，适合实时性要求高的场景，但精度相对较低
• CSRT：精度最高，但速度较慢，适合对精度要求高的场景
• KCF：介于前两者之间，平衡了速度和精度

根据实际应用场景选择合适的跟踪算法可以获得更好的效果。

总结与扩展

本文介绍的目标跟踪系统结合了 YOLO 的强检测能力和传统跟踪算法的高效性，通过简单的交互实现了灵活的目标跟踪功能。该系统可进一步扩展，例如：

• 增加多目标跟踪功能
• 结合 ReID（重识别）技术解决目标遮挡问题
• 加入目标行为分析模块
• 优化跟踪失败后的自动重新检测机制

通过这个系统，不仅可以快速实现实用的目标跟踪应用，也有助于理解目标检测与跟踪相结合的技术路线，为更复杂的计算机视觉系统开发奠定基础。