YOLO-Tracking-Distancing

An implementation of target tracking and distance measurement based on dataset from SAIC.

Installation

Before start, CUDA v10.0 and Cudnn v7.4 are required.

Then install other requirements:

pip3 install -r ./docs/requirements.txt

dog/cat dataset provided by SAIC are supposed to be downloaded to ./data/dataset/dogcat/image/

Then produce labels from xml files

python dataprep_xml2txt.py

or you may just use your own dataset with proper label extraction.

download YOLOV3 pre-trained model trained on coco for transfer learning

cd weights
wget https://pjreddie.com/media/files/yolov3.weights
cd ..

python detection_train.py

It's also possible to train from scratch if following configuration is changed in ./core/config.py

__C.TRAIN.TRAINING_FROM_SCRATCH = False

python detection_test.py  # Detection images are saved in `./data/detection`

The dog & cat are detected and the distance is shown on the boundingboxes

Then the model can be evaluated with

python mAP/result.py  # evaluation results are saved in `./mAP`

For tracking the movement of cat & dog

python tracking.py  # Tracking images are saved in `./data/tracking/image`

To tracking cat & dog in video, just do following configuration in ./core/config.py

__C.TRACKING.INPUT_TYPE           = "video"

Then the tracking images and video are saved in ./data/tracking/video

This work is builded on many excellence works, which including