TY - GEN
T1 - Multiple human detection and tracking by using multiple-stage HOG detector and PFGPDM
AU - Yin, Yafeng
AU - Man, Hong
AU - Desai, Sachi
AU - He, Haibo
PY - 2009
Y1 - 2009
N2 - Detection and tracking of a varying number of people is very essential in surveillance sensor systems. In the real applications, due to various human appearance and confusors, as well as various environmental conditions, multiple targets detection and tracking become even more challenging. In this paper, we proposed a new framework integrating a Multiple-Stage Histogram of Oriented Gradients (HOG) based human detector and the Particle Filter Gaussian Process Dynamical Model (PFGPDM) for multiple targets detection and tracking. The Multiple-Stage HOG human detector takes advantage from both the HOG feature set and the human motion cues. The detector enables the framework detecting new targets entering the scene as well as providing potential hypotheses for particle sampling in the PFGPDM. After processing the detection results, the motion of each new target is calculated and projected to the low dimensional latent space of the GPDM to find the most similar trained motion trajectory. In addition, the particle propagation of existing targets integrates both the motion trajectory prediction in the latent space of GPDM and the hypotheses detected by the HOG human detector. Experimental tests are conducted on the IDIAP data set. The test results demonstrate that the proposed approach can robustly detect and track a varying number of targets with reasonable run-time overhead and performance.
AB - Detection and tracking of a varying number of people is very essential in surveillance sensor systems. In the real applications, due to various human appearance and confusors, as well as various environmental conditions, multiple targets detection and tracking become even more challenging. In this paper, we proposed a new framework integrating a Multiple-Stage Histogram of Oriented Gradients (HOG) based human detector and the Particle Filter Gaussian Process Dynamical Model (PFGPDM) for multiple targets detection and tracking. The Multiple-Stage HOG human detector takes advantage from both the HOG feature set and the human motion cues. The detector enables the framework detecting new targets entering the scene as well as providing potential hypotheses for particle sampling in the PFGPDM. After processing the detection results, the motion of each new target is calculated and projected to the low dimensional latent space of the GPDM to find the most similar trained motion trajectory. In addition, the particle propagation of existing targets integrates both the motion trajectory prediction in the latent space of GPDM and the hypotheses detected by the HOG human detector. Experimental tests are conducted on the IDIAP data set. The test results demonstrate that the proposed approach can robustly detect and track a varying number of targets with reasonable run-time overhead and performance.
KW - Multiple-stage HOG human detector
KW - PFGPDM
KW - Particle filter
UR - http://www.scopus.com/inward/record.url?scp=71649109827&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71649109827&partnerID=8YFLogxK
U2 - 10.1117/12.834896
DO - 10.1117/12.834896
M3 - Conference contribution
AN - SCOPUS:71649109827
SN - 9780819477866
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Unmanned/Unattended Sensors and Sensor Networks VI
T2 - Unmanned/Unattended Sensors and Sensor Networks VI Conference
Y2 - 1 September 2009 through 3 September 2009
ER -