Fast Background Subtraction for Microorganism Detection in Wireless Visual Sensor Networks
With the emergency and development of portable microscopes, applying wireless network and image analysis techniques to real time and intelligent surveillance in sewage treatment system has paid much attention recently. However, the resource limitation and processing capacity are the main constraints in wireless visual sensor networks (WVSNs) which make many background subtraction algorithms for moving object detection unable to work. In this paper, a more efficient and effective detection algorithm based on background subtraction has been proposed. Firstly, a new codebook model is initialized by selecting randomly in the neighbor of spatial domain from the first frame of video sequence, which greatly reduces the modeling time. Secondly, a new color space and a fast local directional pattern texture operator have been introduced into this model to increase the target contrast and neighborhood dependency. Finally, a self adaptive model updating strategy based on statistical parameter estimation has been proposed for adding new code words and deleting noisy code words from models quickly and effectively. Experimental results have demonstrated that our method is able to achieve a better balance between effectiveness and efficiency compared with the state of the art background subtraction algorithms.