Beyond Static Detectors: A Bayesian Approach to Fusing Long-term Motion with Appearance for Robust People Detection in Highly Cluttered Scenes
Jianguo Zhang, Shaogang Gong · 2006
In this work we present a framework for robust people detection in highly cluttered scenes with low resolution image sequences. Our model utilises both human appearance and their long-term motion information through a fusion formulated in a Bayesian framework. In particular, people appearance is modeled by histograms of oriented gradients. Motion information is computed via an improved background modeling by spatial motion constrains. Experiments demonstrate that our method reduces significantly the false positive rate compared to that of a state of the art human detector under very challenging conditions. 1