Region-based scalable smart system for anomaly detection in pedestrian walkways. (May 2019)

• Record Type: Journal Article
• Authors: Murugan, B.S.; Elhoseny, Mohamed; Shankar, K.; Uthayakumar, J.
• Is Part Of: Computers & electrical engineering. Volume 75(2019)
• Page Start: 146
• Page End: 160
• Publication Date: 2019-05
• Subjects: Artificial intelligence -- Anomaly detection -- Convolution neural network -- Computer vision -- Pedestrian walkways; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.02.017
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Different-sized anomalies and its occurrence in a shorter period have always been an open research issue. To resolve the issue of detecting anomalies of different sizes, especially in pedestrian pathways, within a shorter time period, the current research article introduced a Region based Scalable Convolution Neural Network (RS-CNN). The proposed method used region based proposals for faster identification and performed well with the scalability issues. The RS-CNN model was validated using different video sequences from the UCSD anomaly detection dataset. When compared with state-of-the-art detection techniques such as Fast R-CNN, Minimization of Drive Testing (MDT), Mixtures of Probabilistic Principal Component Analyzers (MPPCA) and Social Force (SF), the RS-CNN model was found to be faster and efficient even in the presence of anomalies of various sizes.