Bilattice-based Logical Reasoning for Human Detection

The capacity to robustly detect humans in video is a critical component of automated visual surveillance systems. This paper describes a bilattice based logical reasoning approach that exploits contextual information and knowledge about interactions between humans, and augments it with the output of different low level detectors for human detection. Detections from low level parts-based detectors are treated as logical facts and used to reason explicitly about the presence or absence of humans in the scene. Positive and negative information from different sources, as well as uncertainties from detections and logical rules, are integrated within the bilattice framework. This approach also generates proofs or justifications for each hypothesis it proposes. These justifications (or lack thereof) are further employed by the system to explain and validate, or reject potential hypotheses. This allows the system to explicitly reason about complex interactions between humans and handle occlus...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Vinay D. Shet, Jan Neumann, Visvanathan Ramesh, Larry S. Davis
• Keywords: Computer Vision, CVPR 2007, Histograms Based Detector, Level Parts-based Detectors, Logical Facts, Logical Reasoning Approach, Low Level Detectors