Simple object tracking with OpenCV（centroid tracking with OpenCV）
【原文链接】https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/
【代码下载】https://pan.baidu.com/s/1VVVb2BY-NyTWavO4vLT2lg 提取码：0uc7
opencv single object tracking
https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/
【代码下载链接】https://pan.baidu.com/s/1GTD2dF_OHlfKU_8j5z65rg 提取码：1c4m
【代码使用说明】程序运行时，按下's' 键后视频暂停，等待用户选择跟踪区域。
OpenCV muil-Object Tracking
https://www.pyimagesearch.com/2018/08/06/tracking-multiple-objects-with-opencv/
【代码下载链接】https://pan.baidu.com/s/11jXxXDW0w7L-djxCUY9TbQ 提取码 gg44
OpenCV People Counter
https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/
使用深度学习检测到行人，然后利用中心点就行跟踪匹配
【代码下载链接】https://pan.baidu.com/s/1U8F2mwdM24wfpepTysw8Ug 提取码 pqgs
Windows下利用dlib19.2实现多目标追踪
https://blog.csdn.net/tintinetmilou/article/details/74787994
（不提供代码下载链接）
Multi-object tracking with dlib
https://www.pyimagesearch.com/2018/10/29/multi-object-tracking-with-dlib/
【代码下载链接】https://pan.baidu.com/s/1Mqxu_igniM8h-dhPfQ-a5g 提取码 8sxk
【调试代码时遇到问题】
Boost.Python.ArgumentError for dlib.rectangle #545
File "E:/code/traking/multiobject-tracking-dlib/multi_object_tracking_slow.py", line 112, in <module>
rect = dlib.rectangle(startX, startY, endX, endY)
Boost.Python.ArgumentError: Python argument types in
rectangle.__init__(rectangle, numpy.int32, numpy.int32, numpy.int32, numpy.int32)
did not match C++ signature:
__init__(struct _object * __ptr64, long left, long top, long right, long bottom)
__init__(struct _object * __ptr64)
【暂时解决方案】转化数据类型
https://github.com/davisking/dlib/issues/545
rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
AttributeError: module 'cv2.cv2' has no attribute 'Tracker_create'
【解决方案】需要安装opencv-contrib-python
https://blog.csdn.net/qq_35759574/article/details/82146721
目标跟踪的过程是怎样的？
- 获取目标检测的初始化集合（例如边界框坐标的输入集合）
- 为每一个初始检测对象创建唯一的ID.
- 当这些检测对象在视频帧间移动时跟踪它们，即保持它们分配的ID不变。
Object tracking is the process of:
- Taking an initial set of object detections (such as an input set of bounding box coordinates)
- Creating a unique ID for each of the initial detections
- And then tracking each of the objects as they move around frames in a video, maintaining the assignment of unique IDs
理想的目标跟踪算法是怎样的？
- 只需要对象检测阶段一次（即，当最初检测到对象时）
- 将非常快，比运行实际的对象检测器快得多
- 当被跟踪的对象“消失”或移动到视频帧的边界之外时能够处理
- 在遮挡时稳定检测
- 能够拾取在帧之间“丢失”的对象
An ideal object tracking algorithm will:
- Only require the object detection phase once (i.e., when the object is initially detected)
- Will be extremely fast — much faster than running the actual object detector itself
- Be able to handle when the tracked object “disappears” or moves outside the boundaries of the video frame
- Be robust to occlusion
- Be able to pick up objects it has “lost” in between frames