Received: 27 September 2019 Revised: 6 January 2020 Accepted: 17 January 2020 DOI: 10.1111/coin.12292 SPECIAL ISSUE ARTICLE L 1 norm based pedestrian detection using video analytics technique Anandamurugan Selvaraj 1 Jeeva Selvaraj 1,2 Sivabalakrishnan Maruthaiappan 2 Gokulnath Chandra Babu 3 Priyan Malarvizhi Kumar 4 1 Department of Information Technology, Kongu Engineering College, Erode, India 2 School of Computing Science and Engineering, VIT, Chennai, India 3 School of Computing Science and Engineering, VIT, Vellore, India 4 Department of CSE, Middlesex University, London, UK Correspondence Jeeva Selvaraj, School of Computing Science and Engineering, VIT, Chennai, India Email: sassyjeeva@gmail.com Abstract Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improve- ment in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extrac- tion, and classification. In spite of giving entire infor- mation into feature extraction, the system gives only a useful information (foreground image) by twin back- ground model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For fea- ture extraction, histogram of orientation gradient (HOG) L 1 normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized. KEYWORDS HOG, human detection, pedestrian detection, SVM, twin background model Computational Intelligence. 2020;1–11. wileyonlinelibrary.com/journal/coin © 2020 Wiley Periodicals, Inc. 1