Review on anomalous gait behavior detection using machine learning algorithms

• Published in: Bulletin of Electrical Engineering and Informatics
• Main Author: Razak H.A.; Saleh M.A.M.; Tahir N.M.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087114068&doi=10.11591%2feei.v9i5.2255&partnerID=40&md5=d6cfc3cee025535bd15fef50138bd36d

A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore. © 2020, Institute of Advanced Engineering and Science. All rights reserved.