@article{scholars5683,
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Transactions on Intelligent Transportation Systems},
           pages = {2318--2338},
            year = {2015},
           title = {Vehicle Detection Techniques for Collision Avoidance Systems: A Review},
          volume = {16},
            note = {cited By 323},
          number = {5},
             doi = {10.1109/TITS.2015.2409109},
          author = {Mukhtar, A. and Xia, L. and Tang, T. B.},
            issn = {15249050},
        abstract = {Over the past decade, vision-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 (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries. {\^A}{\copyright} 2000-2011 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959500616&doi=10.1109\%2fTITS.2015.2409109&partnerID=40&md5=9231eb1f9531c33918ae488f883172d4},
        keywords = {Collision avoidance; Motor transportation; Motorcycles; Object detection; Roads and streets; Safety devices; Surveys; Tracking (position), Cluttered environments; Collision avoidance systems; Low cost and reliable; Motorcycle detection; Sensor comparisons; Various illumination conditions; Vehicle detection; Vision-based vehicle detection, Vehicles}
}