Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials Pedestrian tracking in dense crowds is a challenging task, even when using a multicamera 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 systemlevel experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.

DensePeds: Pedestrian Tracking in Dense Crowds Using FrontRVO and Sparse Features We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (u003e2 pedestrians per square meter). Our approach is designed for videos captured from frontfacing or elevated cameras. We present a new motion model called FrontRVO (FRVO) for predicting pedestrian movements in dense situations using collision avoidance constraints and combine it with stateoftheart Mask RCNN to compute sparse feature vectors that reduce the loss of pedestrian tracks (false negatives). We evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd dataset. In practice, our approach is 4.5 xc3x97 faster than prior tracking algorithms on the MOT benchmark and we are stateoftheart in dense crowd videos by over 2.6% on the absolute scale on average.