C. Pertuz, L. Mayron, D. Socek, and O. Marques (USA)
Video segmentation, human detection, object tracking
The field of automated video surveillance has experienced increased research interest due to falling costs of video sensors, increasing security concerns, and the need for improved algorithm for extracting high-level information from video sequences. The analysis of human activities and their environment within the context of security provides information enabling the proactive identification of anomalous behavior. This makes human detection a prerequisite for the automatic extraction of higher level information, such as the recognition of the activities of individual humans. In this paper, we approach the challenge of detecting humans within video sequences as a classification task; moving objects in the foreground are either human or non-human. The classification approach presented in this work is based on motion (periodic motion detection), appearance (skin color detection), and shape (MPEG-7 shape descriptors). A modular infrastructure for data collection, object instantiation, and tracking was also implemented.
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