Automatic video based target tracking with auto swapping and compression technique

Traditional surveillance systems employ static camera assignment to the CCTV operators and automatic processors. Through experiments, it show that this results in under-utilization of the resources as the workload varies over time The workload model is to dynamically change the camera-to-processor assignment to equalize workload Two methods are proposed (IDAS and Spiral) and their performance is compared with the earlier method (DAS). All dynamic load sharing methods provide better equalization than static methods. Usual streaming of entire videos, which could not able to identify the target object Processor capacity is chosen to meet the timing requirement. In this paper, we model the target detection has automatic with the help of the processor and storing the movement of the target in the form of frames and images. This image is converted from the video captured by the web cams connected with the system. Due to the conversion of videos to frames more space to store the videos is reduces. This will help us to monitor and capture the videos of a particular target without any distraction. This paper will also demonstrate the auto swapping and compression technique. Experimental results show that the proposed model successfully captures the variability of the target tracking, and that the automatic target tracking provides better result than the dynamic load sharing methods.