khoitrant68 / deepsort

Re-implementation of Deep SORT.

DeepSORT

Re-implementation of Deep SORT.

Futher improvement direction

• Train detector on specific dataset rather than the official one.
• Retrain REID model on pedestrain dataset for better performance.
• Replace YOLOv3 detector with advanced ones.

Any contributions to this repository is welcome!

Introduction

This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in PAPER is FasterRCNN , and the original source code is HERE.
However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use YOLOv3 to generate bboxes instead of FasterRCNN.

Dependencies

• numpy
• scipy
• opencv-python
• sklearn
• torch
• torchvision
• pillow
• vizer
• edict

Quick Start

0. Check all dependencies installed

pip install -r requirements.txt

1. Clone this repository

git clone [email protected]:KhoiTrant/DeepSORT.git

2. Download YOLOv3 parameters

cd detector/YOLOv3/weight/
wget https://pjreddie.com/media/files/yolov3.weights
wget https://pjreddie.com/media/files/yolov3-tiny.weights
cd ../../../

3. Download deepsort parameters ckpt.t7

cd deep_sort/deep/checkpoint
# download ckpt.t7 from
https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder
cd ../../../

4. Compile nms module

cd detector/YOLOv3/nms
sh build.sh
cd ../../..

5. Run demo

usage: python yolov3_deepsort.py VIDEO_PATH
                                [--help]
                                [--frame_interval FRAME_INTERVAL]
                                [--config_detection CONFIG_DETECTION]
                                [--config_deepsort CONFIG_DEEPSORT]
                                [--display]
                                [--display_width DISPLAY_WIDTH]
                                [--display_height DISPLAY_HEIGHT]
                                [--save_path SAVE_PATH]          
                                [--cpu]          

# yolov3 + deepsort
python yolov3_deepsort.py [VIDEO_PATH]

# yolov3_tiny + deepsort
python yolov3_deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml

# yolov3 + deepsort on webcam
python3 yolov3_deepsort.py /dev/video0 --camera 0

# yolov3_tiny + deepsort on webcam
python3 yolov3_deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0

Use --display to enable display.
Results will be saved to ./output/results.avi and ./output/results.txt.

Training the RE-ID model

The original model used in paper is in original_model.py, and its parameter here original_ckpt.t7.

To train the model, first you need download Market1501 dataset .

Then you can try train.py to train your own parameter and evaluate it using test.py and evaluation.py.

Demo videos and images

demo.avi demo2.avi

References

• paper: Simple Online and Realtime Tracking with a Deep Association Metric

• code: nwojke/deep_sort

• code: ModelBunker/Deep-SORT-PyTorch

• paper: YOLOv3

• code: Joseph Redmon/yolov3