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2006
IEEE

Robust Head Tracking Based on a Multi-State Particle Filter

10 years 5 months ago
Robust Head Tracking Based on a Multi-State Particle Filter
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.
Yuan Li, Haizhou Ai, Chang Huang, Shihong Lao
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where FGR
Authors Yuan Li, Haizhou Ai, Chang Huang, Shihong Lao
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