Enhancing Memory-Based Particle Filter with Detection-Based Memory Acquisition for Robustness under Severe Occlusion

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A novel enhancement for the memory-based particle filter is proposed for visual pose tracking under severe occlusions. The enhancement is the addition of a detection-based memory acquisition mechanism. The memory-based particle filter, called M-PF, is a particle filter that predicts prior distributions from past history of target state stored in memory. It can achieve high robustness against abrupt changes in movement direction and quick recovery from target loss due to occlusions. Such high performance requires sufficient past history stored in the memory. Conventionally, M-PF conducts online memory acquisition which assumes simple target dynamics without occlusions for guaranteeing high-quality histories of the target track. The requirement of memory acquisition narrows the coverage of M-PF in practice. In this paper, we propose a new memory acquisition mechanism for M-PF that well supports application in practical conditions including complex dynamics and severe occlusions. The key idea is to use a target detector that can produce additional prior distribution of the target state. We call it M-PFDMA for M-PF with detection-based memory acquisition. The detection-based prior distribution well predicts possible target position/pose even in limited-visibility conditions caused by occlusions. Such better prior distributions contribute to stable estimation of target state, which is then added to memorized data. As a result, M-PFDMA can start with no memory entries but soon achieve stable tracking even in severe conditions. Experiments confirm M-PFDMA's good performance in such conditions.

Enhancing Memory-Based Particle Filter with Detection-Based Memory Acquisition for Robustness under Severe Occlusion

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