A Bayesian plan-view map based approach for multiple-person detection and tracking
This work proposes a novel approach for people detection and tracking in colour-with-depth sequences using a particle filtering approach. Detection and tracking are performed in plan-view maps integrating occupancy and height information with a novel plan-view map representation for colour information. Using the three maps, we propose a multiple particle filtering algorithm for people detection and tracking. The observation model proposed integrates information from the three maps so that people with different coloured clothes are not confused even when they interact at close distances. To avoid the coalescence problem when people with similar coloured clothes approach each other, the weight of particles is modified by an interaction factor that combines colour and position information. The algorithm also avoids the coalescence problem in case of total occlusion by means of an occlusion detection and recovering mechanism. Finally, a solution is proposed to improve the exponential complexity of multiple particle filters so that the algorithm proposed has linear complexity. The approach proposed has been tested in several colour-with-depth sequences where people move and interact freely in the environment. In the sequences, people walk at different distances, cross their paths causing frequent occlusions, jump, run and have close interactions such as shaking hands or embracing each other. The experimental results show that our proposal is able to detect and keep track of every person with a low error and deals with partial and total occlusions. Besides, the detection and tracking techniques presented are appropriate for large tracking problems in real-time applications since their complexity is linear, are suitable for parallel processing and allow the integration of information provided by multiple stereo vision sensors.
plan-view maps particle filtering stereo vision person tracking colour processing condensation algorithm