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Robust Head Tracking with Particles Based on Multiple Cues Fusion

  • Yuan Li
  • Haizhou Ai
  • Chang Huang
  • Shihong Lao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3979)

Abstract

This paper presents a fully automatic and highly robust head tracking algorithm based on the latest advances in real-time multi-view face detection techniques and multiple cues fusion under particle filter framework. Visual cues designed for general object tracking problem hardly suffice for robust head tracking under diverse or even severe circumstances, making it a necessity to utilize higher level information which is object-specific. To this end we introduce a vector-boosted multi-view face detector [2] as the “face cue” in addition to two other general visual cues targeting the entire head, color spatiogram[3] and contour gradient. Data fusion is done by an extended particle filter which supports multiple distinct yet interrelated state vectors (referring to face and head in our tracking context). Furthermore, pose information provided by the face cue is exploited to help achieve improved accuracy and efficiency in the fusion. Experiments show that our algorithm is highly robust against target position, size and pose change as well as unfavorable conditions such as occlusion, poor illumination and cluttered background.

Keywords

Face Detection Color Histogram Observation Model Cluttered Background Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuan Li
    • 1
  • Haizhou Ai
    • 1
  • Chang Huang
    • 1
  • Shihong Lao
    • 2
  1. 1.Department of Computer ScienceTsinghua UniversityBeijingChina
  2. 2.Sensing and Control Technology LabOMRON CorporationKyotoJapan

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