MyJournals Home  

RSS FeedsSensors, Vol. 20, Pages 628: Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials (Sensors)

 
 

23 january 2020 00:04:45

 
Sensors, Vol. 20, Pages 628: Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials (Sensors)
 


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.


 
222 viewsCategory: Chemistry, Physics
 
Sensors, Vol. 20, Pages 627: Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty (Sensors)
Sensors, Vol. 20, Pages 626: Simulation-Based Design and Optimization of Rectangular Micro-Cantilever-Based Aerosols Mass Sensor (Sensors)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten