caa a22 4500 605468362 CHVBK 20210128100316.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1375-9 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1375-9 Multiple pedestrian tracking based on couple-states Markov chain with semantic topic learning for video surveillance [Elektronische Daten] [Peng Zhang, Liang Wang, Wei Huang, Lei Xie, Guang Chen] Robust, accurate and efficient pedestrian tracking is a critical task in intelligent visual surveillance systems and robotic vision applications. Unfortunately, tracking in realistic scenarios is not easy and may fail due to the challenges of partial occlusion, viewpoint changes and cluttered background. To overcome these challenges, different tracking strategies have been proposed to model the tracking process as a first-order temporal Markov chain. It has been well-known that target appearance modeling approaches play essential function in this process, but most these approaches only represent the object characteristics in the pixel/texture level instead of investigating the latent information in the semantic understanding level. Therefore, the obtained optimum state based on the texture similarity may not be as same as observed by human vision system. To resolve this limitation, in this paper, we proposed a multiple pedestrian tracking algorithm based on couple-states analysis, the hidden state is used to obtain the estimated observations during the Markov chain transition process, and the latent state is used to find the semantic information about each observation. By maximizing the likelihood probability of the couple states for each estimation, the optimum state of target can be found more accurately, and error accumulation can also be effectively decreased during tracking. The performance of the proposed tracking has been verified on different benchmark surveillance video database, the results showed that the proposed tracking is able to track multiple pedestrians more accurately in variety of challenge scenarios when compared with other state-of-art multiple pedestrian tracking approaches. Springer-Verlag Berlin Heidelberg, 2014 Multiple pedestrian nationallicence Tracking nationallicence Couple states nationallicence Semantic topic learning nationallicence Video surveillance nationallicence Zhang Peng School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut Wang Liang ASML US, Inc., Chandler, USA aut Huang Wei School of Information Engineering, Nanchang University, Nanchang, China aut Xie Lei School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut Chen Guang Xi'an Communications Institute, Xi'an, China aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 85-97 1432-7643 19:1<85 2015 19 500 https://doi.org/10.1007/s00500-014-1375-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1375-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Peng School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Wang Liang ASML US, Inc., Chandler, USA aut NATIONALLICENCE 700 1- Huang Wei School of Information Engineering, Nanchang University, Nanchang, China aut NATIONALLICENCE 700 1- Xie Lei School of Computer Science, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Chen Guang Xi'an Communications Institute, Xi'an, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 85-97 1432-7643 19:1<85 2015 19 500