A survey on wireless multimedia sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cluster Analysis and Optimization in Color-Based Clustering for Image Abstract
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Wireless video-based sensor networks for surveillance of residential districts
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Information-intensive wireless sensor networks: potential and challenges
IEEE Communications Magazine
An association rule analysis framework for complex physiological and genetic data
HIS'12 Proceedings of the First international conference on Health Information Science
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Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering algorithms such as DBSCAN cannot run time-efficiently and even fail to run on limited memory space on sensor nodes, because the number of pixel points is too huge. This paper provides a novel algorithm named Inter-Frame Change Directing Online clustering (IFCDO clustering) for segmenting multiple moving objects from video stream on sensor nodes. IFCDO clustering only needs to group inter-frame different pixels, thus it reduces both space and time complexity while achieves robust clusters the same as DBSCAN. Experiment results show IFCDO clustering excels DBSCAN in terms of both time and space efficiency.