Abstract
Safety on roads and the prevention of accidents have become major problems in the world. Intelligent cars are now a standard in the future of transportation. Drivers will benefit from the increased support for driving assistance. This means relying on the development of integrated systems that can provide real-time information to help drivers make decisions. Therefore, computer vision systems and algorithms are needed to detect and track vehicles. This helps traffic management and driving assistance. This paper focuses on developing a reliable vehicle tracking system to detect the vehicle that is following the host vehicle. The proposed system uses a unique approach consisting of a mixture of background removal techniques, Haar features in a modified Adaboost algorithm in a cascade configuration, and SURF descriptors for tracking. From the camera mounted at the rear of the host vehicle, videos are captured. The results presented in this paper demonstrate the potential and efficiency of the system.
Highlights
As the number of vehicles increases, the demand for driver assistance systems increases, ensuring safe and comfortable driving
The vision system installed in the transport vehicle can provide the location and size of other vehicles in the traffic environment, as well as provide information from roads, traffic lights, and other road users
To update the background model, based their approach on the texture of the image, using processing such as the texture-based moving object detection method (TBMOD), in which each distribution is adjusted by weight [8]
Summary
As the number of vehicles increases, the demand for driver assistance systems increases, ensuring safe and comfortable driving. To update the background model, based their approach on the texture of the image, using processing such as the texture-based moving object detection method (TBMOD), in which each distribution is adjusted by weight [8]. 65% higher and false detection occurs when using a car with sunlight and spectral values Engel and his team proposed a multi-object tracking approach which is based on a cascade filter of detector objects [10]. The system will be evaluated through standard performance measures, comparison with other similar systems, its effectivness against occlusion, and its effectiveness when presented with different vehicle shapes as well as varying external conditions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
