搭建环境Ubuntu16.04+CUDA9+cudnn7+python3.5+源码编译Tensorflow1.4+opencv3.3
基于 darkflow (yolo-v2)和 sort/deep_sort 实现目标检测与追踪
Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow
安装依赖#sort$sudo pip install numba$sudo pip install matplotlib$sudo apt-get install python-tk$sudo pip install scikit-image$sudo pip install filterpy$sudo pip install sklean#dark_sort$sudo pip install Cython$sudo pip install scipy$sudo pip install sklean配置环境$git clone https://github.com/bendidi/Tracking-with-darkflow.git$git submodule update --init --recursive$cd darkflow/$python setup.py build_ext --inplace在darkflow下新建bin目录
$mkdir bin$cd bin$wget https://pjreddie.com/media/files/yolo.weights #获取yolo-v2训练好的权重用于检测你可以在 yolo 官网 下载配置文件和训练权重。
$cd ../..$cd darksort/下载 resource压缩包并解压至 darksort/
链接（需要**且速度较慢，300多M）
配置参数from darkflow.darkflow.defaults import argHandler #Import the default argumentsimport osfrom darkflow.darkflow.net.build import TFNetFLAGS = argHandler()FLAGS.setDefaults()FLAGS.demo = "camera" # 你需要检测的视频文件 默认为你的摄像头 "camera"FLAGS.model = "darkflow/cfg/yolo.cfg" # tensorflow modelFLAGS.load = "darkflow/bin/yolo.weights" # tensorflow weights# FLAGS.pbLoad = "tiny-yolo-voc-traffic.pb" # tensorflow model# FLAGS.metaLoad = "tiny-yolo-voc-traffic.meta" # tensorflow weightsFLAGS.threshold = 0.7 # threshold of decetion confidance (detection if confidance > threshold )FLAGS.gpu = 0.8 #how much of the GPU to use (between 0 and 1) 0 means use cpuFLAGS.track = True # 置为 True 表示启用目标追踪， False 表示仅启用目标检测#FLAGS.trackObj = ['Bicyclist','Pedestrian','Skateboarder','Cart','Car','Bus'] # the object to be tracked FLAGS.trackObj = ["person"]FLAGS.saveVideo = True #是否保存检测后的视频文件到当前目录FLAGS.BK_MOG = True # activate background substraction using cv2 MOG substraction, #to help in worst case scenarion when YOLO cannor predict(able to detect mouvement, it's not ideal but well) # helps only when number of detection < 3, as it is still better than no detection.FLAGS.tracker = "deep_sort" # wich algorithm to use for tracking deep_sort/sort (NOTE : deep_sort only trained for people detection )FLAGS.skip = 0 # how many frames to skipp between each detection to speed up the networkFLAGS.csv = False #whether to write csv file or not(only when tracking is set to True)FLAGS.display = True # display the tracking or nottfnet = TFNet(FLAGS)tfnet.camera()exit('Demo stopped, exit.')- 1
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如果你没有用于测试的视频文件，你可以获取 MOT数据集 并下载到本地。
开始$python run.py
如果没有出现错误，你可以顺利完成对自己视频文件中的行人追踪。
