View Invariant Motorcycle Detection for Helmet Wear Analysis in Intelligent Traf fic Surveillance M. Ashvini, G. Revathi, B. Yogameena and S. Saravanaperumaal Abstract An important issue for intelligent traf fic surveillance is automatic vehicle classification in traf fic scene videos, which has great prospective for all kinds of security applications. Due to the number of vehicles in operation surpassed, occurrence of accidents is increasing. Hence, the vehicle classification is an important building block of surveillance systems that significantly impacts relia- bility of its applications. It helps in classifying the motorcycles that uses public transportation. This has been identified as an important task to conduct surveys on estimation of people wearing helmets, accident with and without helmet and vehicle tracking. The inability of police power in many countries to enforce helmet laws results in reduced usage of motorcycle helmets which becomes the reason for head injuries in case of accidents. This paper comes up with a system with view invariant using Histogram of Oriented Gradients which automatically detects motorcycle riders and determines whether they are wearing helmets or not. Keywords Background subtraction ⋅ Histogram of Oriented Gradients (HOG) ⋅ Center-Symmetric Local Binary Pattern (CS-LBP) ⋅ K-Nearest Neighbor (KNN) M. Ashvini ( ✉ ) ⋅ G. Revathi ⋅ B. Yogameena Department of ECE, Thiagarajar College of Engineering, Madurai, India e-mail: ashvinimano@gmail.com G. Revathi e-mail: rev.gsa@gmail.com B. Yogameena e-mail: b.yogameena@gmail.com S. Saravanaperumaal Department of Mechanical, Thiagarajar College of Engineering, Madurai, India e-mail: sfpmech@gmail.com © Springer Science+Business Media Singapore 2017 B. Raman et al. (eds.), Proceedings of International Conference on Computer Vision and Image Processing, Advances in Intelligent Systems and Computing 460, DOI 10.1007/978-981-10-2107-7_16 175