Joint tracking and gait recognition of multiple people in video

We propose a novel approach to address the problem of jointly tracking and gait recognition of multiple people in a video sequence. The most state of the art algorithms for gait recognition consider the cases where there is only one person without any occlusion in a very constrained environment. However, in real scenarios such as in airports, train stations, etc, there are many people in the environment that make these algorithms inapplicable. Although first tracking of each person and then gait recognition could be a solution, we argue that the multi-people tracking and the gait recognition in a video are two sub-problems that can help each other. Hence, we propose a joint tracking and gait recognition of multiple people as one framework that can improve gait recognition accuracy and decrease the ID switching in tracking. Experimental results confirm the validity of proposed approach.