Hiromu Hasegawa

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Fast algorithm of SMC-PHD filter for track-before-detection in visual tracking by eliminating other targets from the original image has been proposed. The elimination allows us to approximate the original likelihood term in PHD filter to an simplified one leading to a fast algorithm having computational complexity proportional to the number of particles,(More)
Performance evaluation of multiple pedestrian tracking with/without the particle filter technique has been conducted by proposing some elaborated criteria for evaluation in terms of 1) detection evaluation for each frame, and 2) tracking evaluation for each image sequence. We cope with non-triviality on performance evaluate of multiple pedestrians detection(More)
Human moving behaviors such as walking, running, standing, sitting, taking stairs, are to be estimated from 3-axis acceleration signal of smart phone's sensor. For this purpose, state estimation via particle filter with Self-Organizing Map (SOM) based likelihood has been proposed. Input signal is the length of 3 dimensional vector of 3-axis acceleration(More)
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(More)
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