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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 memorybased particle filter, M-PF, is a particle filter that predicts prior distributions from past history of target state, which achieved high robustness against complex dynamics of a tracking target. Such high performance requires sufficient history stored in memory. Conventionally, M-PF conducts online memory acquisition which assumes simple target’s dynamics without occlusions for guaranteeing high quality histories. 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. 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 under severe occlusions. Experiments confirm M-PFDMA’s good performance in such

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