Fusion of multi-modal features in particle filter with dual weight of belief and plausibility for track-before-detect multiple target tracking

Dual weight of belief and plausibility have been introduced to cope with fusion problem of multi-modal features in observation process within a framework of track-before-detect visual tracking by particle filter for multiple target. Observation model consists of dual function of belief and plausibility corresponding to conjunction and disjunction of multi-modal features. Each particle has dual weight corresponding to the two likelihood functions, and the two weights are updated respectively. Resampling step involves some elaborations consisting of three steps such that; 1) normalized weights of plausibility are used as the probability to draw with replacement, 2) uniform value is set for the weights of plausibility after the draw with replacement, and 3) weights of belief are adjusted for each particle. The idea of dual weight has been extended to multiple target tracking framework with SMC-PHD filter. Performance of the proposed method has been demonstrated for multiple people tracking over videos captured by a fish eye camera at ceiling.