Real-time object tracking and surveillance using a parallel computeing Architecture

Abstract

Closed-circuit television (CCTV) cameras are used widely in surveillance applications where operators need to constantly monitor the videos on the video wall. The objective of this research is to improve the efficiency of the personal who monitor the videos in vehicle surveillance applications. Two types of vehicle surveillance are considered: the detection of vehicles coming to a stop, and trackingmoving vehicles through multiple cameras. The event of a vehicle coming to a stop occurs in situations such as vehicles stop at the toll plaza at express ways or car parks. The purpose of detecting a vehicle coming to a stop is to minimize frauds which may occur during the toll collection process. The approach to minimize such frauds is by using the vehicle count as a reference. The use ofGraphics ProcessingUnit (GPU)s to process the videos reduces the average execution time from0.096s to 0.075s. The detection and tracking moving vehicle through multiple cameras are considered as the second type of vehicle surveillance. These multiple cameras are fixed in different locations and the same vehicle may appear on different cameras in different times. It is a tedious process to manually track these vehicles through nonoverlapping cameras. In the approach of tracking moving vehicles throughmultiple cameras the processing power of GPUs are used. GPUs parallelize the detection algorithm to achieve the real time performance for two video streams which are processed concurrently. The algorithm which matches the vehicles through multiple cameras gives an accuracy of over 80%. In the events of detecting a vehicle coming to a stop and detecting and tracking moving vehicles through multiple cameras, the processing power of GPUs are used to reduce the processing time of a frame to achieve the real time performance.