Object detection using deep learning and multi-object tracking

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

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

1. Open the terminal
2. Go to the tensorflow_model_dir: cd ./tensorflow_model_dir
3. Run: sudo chmod +x ./get_ssd_model.sh
4. Run: ./get_ssd_model.sh

This will download model and config files in ./tensorflow_model_dir. These will be used tracking-tensorflow-ssd_mobilenet_v2_coco_2018_03_29.ipynb.

SSD-Mobilenet_v2_coco_2018_03_29 was used for this example. Other networks can be downloaded and ran: Go through tracking-tensorflow-ssd_mobilenet_v2_coco_2018_03_29.ipynb for more details.

• 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").

Video used in SSD-Mobilenet multi-object detection and tracking: https://flic.kr/p/26WeEWy

• 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").

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.

Suggestion: If you are looking for speed go for SSD-mobilenet. If you are looking for accurracy and speed go with YOLO. The best way is to train and fine tune your models on your dataset. Although, Faster-RCNN gives more accurate object detections, you will have to compromise on the detection speed as it is slower as compared to YOLO.

If you use this repository in your work, please consider citing it with:

@misc{multiobjtracker_amd2018,
  author = {Deshpande, Aditya M.},
  title = {multi-object-tracker},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/adipandas/multi-object-tracker}},
}