Object tracking using a pre-trained object detection model & the dlib correlation tracker.

The algorithm starts streaming from a video file or a live web-cam feed. Object detection through a pre-trained model is performed on the feed till a positive for the input label is found.

The correlation tracker used here uses discriminative correlation filters to localize the target, building upon a Minimum Output Sum of Squared Error (MOSSE) filter. This makes the tracker robust to variations in lighting, pose & scale.

More information about the tracking algorithm can be found below:

1. Object Tracking in OpenCV
2. Visual Object Tracking using Adaptive Correlation Filters
3. Accurate Scale Estimation for Robust Visual Tracking

The detection result is taken to be as the single object with the best detection confidence.

A tracker is subsequently placed on the video stream and is updated every frame to track the object.

python single_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --label person
python single_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --label person --out output.avi
python single_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --video test.mp4 --label person --out output.avi

All the detection results for the input label are tracked to provide multiple detections.

The tracking processes are distributed via python's multiprocessing module.

python multi_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --label person
python multi_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --label person --out output.avi
python multi_object_tracking.py --prototxt model\MobileNetSSD_deploy.prototxt --model model\MobileNetSSD_deploy.caffemodel --video test.mp4 --label person --out output.avi