Track COCO objects

A tool that tracks the COCO objects in a video. This repo uses the Yolov8 object detection model provided by Ultralytics and the Simple Online Realtime Tracking(SORT) algorithm provided by sort.

Installation and Processing Steps

Steps to install and use in Ananconda

• conda create --name trackCOCOObjects python=3.8
• conda activate trackCOCOObjects
• git clone https://github.com/rgkannan676/Track-COCO-Objects.git
• cd Track-COCO-Objects
• Install the latest PyTorch from 'https://pytorch.org/' example: 'conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia'
• Install the required libraries: pip install -r requirements.txt
• Download yolov8 pytorch checkpoint model yolov8m.pt provided by Ultralytics and copy to 'yolo' folder.
• Copy the videos to covert in the folder 'video_input'
• Run 'python main.py'. This will start the processing.
• See the output videos in folder 'video_output' . The video will contain object detection results with a tracking id for each coco object.

Adjustable Configs

Can change the below configs in main.py.

• YOLO_CHECK_POINT: Yolov8 has different types of models like yolov8x.pt, yolov8m.pt, yolov8n.pt etc. Can download and change this config.
• YOLO_MODEL_DEVICE: Device to run detection 0,1,2 etc.. for cuda or 'cpu' for cpu.
• YOLO_CONFIDENCE_THRESHOLD: Confidence threshold of Yolov8 detection model.
• SORT_MAX_AGE: Max life period where unmatched tracker object exists.
• SORT_MIN_HIT: Minimum number of hit_streaks(total number of times it consecutively got matched with detection in the last frames) such that it gets displayed in the outputs.
• SORT_IOU: IOU threshold used for SORT algorithm.

Result

• Check sample/sample.mp4

sample.mp4