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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, we propose a novel 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 intercorrelated 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 can easily incorporate various image cues (e.g., edge intensity, foreground region color and background region color), and also offers an online 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.