A Structural Filter Approach to Human Detection

Occlusions and articulated poses make human detection much more difficult than common more rigid object detection like face or car. In this paper, a Structural Filter (SF) approach to human detection is presented in order to deal with occlusions and articulated poses. A three-level hierarchical object structure consisting of words, sentences and paragraphs in analog to text grammar is proposed and correspondingly each level is associated to a kind of SF, that is, Word Structural Filter (WSF), Sentences Structural Filter (SSF) and Paragraph Structural Filter (PSF). A SF is a set of detectors which is able to infer what structures a test window possesses, and specifically WSF is composed of all detectors for words, SSF is composed of all detectors for sentences, and so as PSF. WSF works on the most basic units of an object. SSF deals with meaningful sub structures of an object. Visible parts of human in crowded scene can be head-shoulder, left-part, right-part, upper-body or whole-body, and articulated human change a lot in pose especially in doing sports. Visible parts and different poses are the appearance statuses of detected humans handled by PSF. The three levels of SFs, WSF, SSF and PSF, are integrated in an embedded structure to form a powerful classifier, named as Integrated Structural Filter (ISF). Detection experiments on pedestrian in highly crowded scenes and articulated human show the effectiveness and efficiency of our approach.