multi-object-tracker

object detection using deep learning and multi-object tracking

The work here is based on the following literature available:

1. http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf
2. Pyimagesearch 1, 2
3. correlationTracker
4. Caffemodel zoo
5. Caffemodel zoo GitHub
6. YOLO v3

Use the caffemodel zoo from the reference [4,5] mentioned above to vary the CNN models and Play around with the codes.

Install OpenCV

Pip install for OpenCV (version 3.4.3 or later) is available here and can be done with the following command:

pip install opencv-contrib-python

Run with YOLO

1. Open the terminal
2. Go to yolo_dir in this repository: cd ./yolo_dir
3. Run: sudo chmod +x ./get_yolo.sh
4. Run: ./get_yolo.sh

The model and the config files will be downloaded in ./yolo_dir. These will be used tracking-yolo-model.ipynb.

• The video input can be specified in the cell named Initiate opencv video capture object in the notebook.
• To make the source as the webcam, use video_src=0 else provide the path of the video file (example: video_src="/path/of/videofile.mp4").

Example video used in above demo: https://flic.kr/p/L6qyxj

Run with Caffemodel

• You have to use tracking-caffe-model.ipynb.
• The model for use is provided in the folder named caffemodel_dir.
• The video input can be specified in the cell named Initiate opencv video capture object in the notebook.
• To make the source as the webcam, use video_src=0 else provide the path of the video file (example: video_src="/path/of/videofile.mp4").