Region-based scalable smart system for anomaly detection in pedestrian walkways. (May 2019) - Murugan, B.S.; Elhoseny, Mohamed; Shankar, K.; Uthayakumar, J.
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.