Multi-Object-Tracker

Object detection using deep learning and multi-object tracking

YOLO

SSD

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 TensorFlow SSD model

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

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").

References

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.