JPDAF based HMM for real-time contour tracking

Yunqiang Chen, Yong Rui, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review


Tracking objects using multiple cues yields more robust results. The well-known hidden Markov model (HMM) provides a powerful framework to incorporate multiple cues by expanding its observation. However, a plain HMM does not capture the inter-correlation between measurements of neighboring states when computing the transition probabilities. This can seriously damage the tracking performance. To overcome this difficulty, in this paper, we propose a new HMM framework targeted at contour-based object tracking. A joint probability data association filter (JPDAF) is used to compute the HMM's transition probabilities, taking into account the inter-correlated neighboring measurements. To ensure real-time performance, we have further developed an efficient method to calculate the data association probability via dynamic programming, which allows the proposed JPDAF-HMM to run comfortably at 30 frames/sec. This new tracking framework not only can easily incorporate various image cues (e.g., edge intensity, foreground region color and background region color), but also offers an on-line learning process to adapt to changes in the scene. To evaluate its tracking performance, we have applied the proposed JPDAF-HMM in various real-world video sequences. We report promising tracking results in complex environments.

Original languageEnglish (US)
Pages (from-to)I543-I550
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - Dec 1 2001
Event2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Kauai, HI, United States
Duration: Dec 8 2001Dec 14 2001

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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