Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
Smart objects identification system for robotic surveillance
International Journal of Automation and Computing
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The problem of multiple people detection in monocular video streams is addressed. The proposed method involves a human model based on skin color and foreground information. Robustness to local motion of background and global color changes is achieved by modeling images as fields of color distributions, and robustly estimating temporal background global variations. The estimation of the human model parameters is done via Monte Carlo simulations to deal with the multimodal nature of the posterior distribution, introduced by the presence of multiple people and cluttered scene. Promising results are presented for transportation vehicles sequences.