Video parsing based on head tracking and face recognition
Proceedings of the 6th ACM international conference on Image and video retrieval
Tsinghua Face Detection and Tracking for CLEAR 2007 Evaluation
Multimodal Technologies for Perception of Humans
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
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This paper proposes a novel method for robust and automatic realtime head tracking by fusing face and head cues within a multi-state particle filter. Due to large appearance variability of human head, most existing head tracking methods use little object-specific prior knowledge, resulting in limited discriminant power. In contrast, face is a distinct pattern much easier to capture, which motivates us to incorporate a vector-boosted multi-view face detector[6] to lend strong aid to general head observation cues including color and contour edge. To simultaneously and collaboratively perform temporal inference of both the face state and the head state, a Markov-network-based particle filter is constructed using sequential belief propagation Monte Carlo[5]. Our approach is tested on sequences used by previous researchers as well as on new data sets which includes many challenging real-world cases, and shows robustness against various unfavorable conditions.