A Novel vision based embedded framework system to detect and track dynamic vehicles

Abstract

Abstract A vehicle tracking system is a smart device installed in vehicles to detect and track the moving vehicle. In the emerging world the number of vehicles on road has been increasing globally. Intelligent transport system has been explored better to make an efficient transport system and also for traffic arrangements. Hence there is a need for a system that tracks the vehicle and provide assistance to the driver. The study aims at developing a novel embedded driver assistance framework that could analyze the dynamics of vehicle in the front and rear surround views. In the proposed study, to detect and track the moving vehicles Gaussian Mixture Model (GMM) and Kalman filter is implemented thereby critical safety to the vehicles is enabled by calculating the distance along with the directions. The developed prototype contains 4 cameras each equipped with an embedded processor. In order for the system to run at a near real time pace, optimized vision-based techniques are used that detect vehicles that are near-by. The only modality used for sensing are the camera sensors which analyze the surroundings of the ego or the host vehicle. Object detection by GMM technique proves to be more accurate and reliable in light traffic conditions and during day time whereas during night time the detection technique is not that accurate. Hence the developed prototype includes ultrasonic sensors that calculates the distance between the objects by sound wave. Further the developed detection system is validated by analyzing Receiver Operating Characteristics. Thus vision based embedded computing framework proves to be more efficient method for tracking objects.