Deep MDP: A Modular Framework for Multi-Object Tracking
This provides a flexible and user-friendly tool for researchers and practitioners in computer vision to experiment with MOT applications, but it is incremental as it builds on existing paradigms without major innovations.
The paper presents Deep MDP, a modular framework for Multi-Object Tracking (MOT) based on the Markov decision process tracking-by-detection paradigm, designed to allow easy replacement of components and includes an interactive GUI for detection, segmentation, and labeling, though it does not achieve new performance breakthroughs.
This paper presents a fast and modular framework for Multi-Object Tracking (MOT) based on the Markov descision process (MDP) tracking-by-detection paradigm. It is designed to allow its various functional components to be replaced by custom-designed alternatives to suit a given application. An interactive GUI with integrated object detection, segmentation, MOT and semi-automated labeling is also provided to help make it easier to get started with this framework. Though not breaking new ground in terms of performance, Deep MDP has a large code-base that should be useful for the community to try out new ideas or simply to have an easy-to-use and easy-to-adapt system for any MOT application. Deep MDP is available at https://github.com/abhineet123/deep_mdp.