Abstract
Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.
Highlights
Video multiple object tracking (MOT) is widely used in computer vision research applications, including video surveillance, traffic detection, and robotic assistance
A cross-view tracklet measurement method based on image mutual information is proposed, which can more accurately describe the spatial relationship between the cross-view tracklets and obtain a multi-view fusion tracklet coupling set through an iterative generation algorithm
We propose a spatial similarity measurement method that can comprehensively consider the position and motion information of two tracklets; that is, the method uses the image mutual information to calculate the spatial relationship between the tracklets
Summary
Video multiple object tracking (MOT) is widely used in computer vision research applications, including video surveillance, traffic detection, and robotic assistance. Due to the group motion characteristics of dense pedestrians, the complete occlusion points typically change with changes in time and position, which may result in a large number of short tracklets when the trajectory fragments are established With insufficient information, these short tracklets cannot provide reliable features of the objects, resulting in a decline in data association performance. The multi-camera tracking system builds cross-view tracklet couplings with a new data fusion method, and links them by association algorithm based on a new Markov random field (MRF) model. The MRF model contains a new potential function improvement method for dense pedestrian crowd scenarios, including assimilation as well as extension processing and a message selective belief propagation (MSBP) algorithm The former enhances the information of the short tracklet coupling and expands it through information sharing. Related works in Section 2; generation of cross-view tracklet coupling is described in Section 3; system framework and Markov random field model for data association are introduced in Section 4; experiments are shown in Section 5; Section 6 is the discussion and conclusion; the last section is the Appendix A
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
