Tracking objects in video is a thoroughly studied problem in computer vision that has important applications in industries like sports, retail and security. There are several possible approaches to this problem, but a popular one that’s both simple to implement and effective in practice is called tracking-by-detection.
The tracking-by-detection paradigm relies heavily on high quality object detectors. This means it can leverage advances in deep learning that have dramatically improved the performance of these models.
In this post, we’ll walk through an implementation of a simplified tracking-by-detection algorithm that uses an off-the-shelf detector available for PyTorch. If you want to play with the code, check out the algorithm or the visualization on GitHub.[Read More]