TY - JOUR
T1 - People detection in low-resolution video with non-stationary background
AU - Zhang, Jianguo
AU - Gong, Shaogang
PY - 2009
Y1 - 2009
N2 - In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].
AB - In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].
UR - http://www.scopus.com/inward/record.url?scp=57949114980&partnerID=8YFLogxK
U2 - 10.1016/j.imavis.2008.06.013
DO - 10.1016/j.imavis.2008.06.013
M3 - Article
AN - SCOPUS:57949114980
SN - 0262-8856
VL - 27
SP - 437
EP - 443
JO - Image and Vision Computing
JF - Image and Vision Computing
IS - 4
ER -