A smart surveillance system using visual sensors: poster abstract

Designing a smart surveillance system consisting of directional visual sensors is challenging because of the need of optimal selection of communication sectors and sensing sectors of the sensors in a particular time frame. Detecting a moving target is a primary task of a surveillance system. However, if all of the sensors are active for all the time sensors will be out of the power and surveillance system will not sustain. Therefore, it is important to find the minimum number of sensors to provide the required coverage and to detect the target properly. For better moving target prediction and reducing computational cost at the minimum, we use low cost Kalman Filter. The simulation results show that our proposed mechanism can be a promising research idea.