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PROJECT TITLE :

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

ABSTRACT:

Over the past decade, vision-primarily based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (significantly for motorcycles), cluttered environment, numerous illumination conditions, and driving behavior. In this paper, we have a tendency to give a comprehensive survey during a systematic approach concerning the state-of-the-art on-road vision-based mostly vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based mostly on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each method/step are reviewed and analyzed individually. 2 main contributions during this paper are the following: survey on motorcycle detection techniques and also the sensor comparison in terms of price and vary parameters. Finally, the survey provides an optimal selection with a low value and reliable CAS design in vehicle industries.


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