Intelligent video surveillance system deal's with the real-time monitoring of persistent and transient objects within a specific environment. In existing video surveillance using CCTV (close circuit television) works with binary segmentation algorithm and it had critical pre processing steps in various high level computer vision application. This can be applied not only in security systems, but also uses in environmental surveillance. The basic principle of moving object detecting is given by the Background Subtraction algorithm. Then, a self-adaptive background model that can update automatically and timely to adapt to the slow and slight changes of natural environment is detailed. When the subtraction of the current captured image and the background reaches a certain threshold, a moving object is considered to be in the current view, and the mobile phone will automatically notify the central control unit and automatic alerting system alert the authorized user through SMS and user can view the detected image by GPRS enabled mobile devices.
One of the most significant strategy measures of wireless sensor networks (WSNs) is energy efficiency. Grouping affords an effective way for encompassing the lifetime of the network. We offer a double cluster-heading clustering algorithm using particle swarm optimization (PSO-DH). The algorithm computes two cluster skulls. The determination of the dominant cluster and the immorality cluster-head needs consider the state information, including position and energy reservation about nodes and their neighbors. Because every node contains a list of information about his neighbors and location using connected dominating set. The dominant cluster head (DCH) receives and masses data to analyst directly. The algorithm poises the energy consumption, so it can encompass the network life time effectively.
Show abstract
Full Text
