Multi-Object-Tracking

General Idea

A multi-object-tracking algorithm uses YOLO v3, deep_sort and optical flow based on Kanade–Lucas–Tomasi (KLT).

Methodology

1. YOLO v3 detection
2. deep_sort tracker update
3. optical flow tracker update

Dependences

The code has been tested in python 3.5, ubuntu 16.04.

1. tensorflow
2. keras
3. numpy
4. sklearn
5. scipy
6. scikit-image
7. opencv

How to run

1. Download yolov3 model from YOLO website. Convert this model to a Keras model. For this project, we train a new yolov3 model and use Keras.save_model.
2. Run script: python3.5 tracking.py

Results

1. test result video 1: https://youtu.be/SKX-EcQnens
2. test result video 2: https://youtu.be/56RKbOaInYI

Reference work

1. keras YOLO v3: https://github.com/qqwweee/keras-yolo3
2. deep_sort: https://github.com/nwojke/deep_sort
3. YOLO v3 deep_sort integration: https://github.com/Qidian213/deep_sort_yolov3
4. optical flow: https://github.com/ZheyuanXie/OpticalFlow