Efficient and Accurate Object Classification in Wireless Multimedia Sensor Networks

Object classification from video frames has become more challenging in the context of Wireless Multimedia Sensor Networks (WMSNs). This is mainly due to the fact that these networks are severely resource constrained in terms of the deployed camera sensors. The resources refer to battery, processor, memory and storage of the camera sensor. Limited resources mandates the need for efficient classification techniques in terms of energy consumption, space usage and processing power. In this paper, we propose an efficient yet accurate classification algorithm for WMSNs using a genetic algorithm-based classifier. The efficiency of the algorithm is achieved by extracting two simple but effective features of the objects from the video frames, namely shape of the minimum bounding box of the object and the speed of the object in the monitored region. The accuracy of the classification, on the other hand, is provided through using a genetic algorithm whose space/memory requirements are minimal. The training of this genetic algorithm based classifier is done offline and it is stored at each camera in advance to perform online classification during surveillance missions. The experiments indicate that a promising classification accuracy can be achieved without introducing a major energy and storage overhead on camera sensors.