Mask Region Based Convolution Neural Network (R-CNN) based Smart System for Anomaly Detection in Pedestrian Walkways
A. Nirmala1, S. Arivalagan2, P. Sudhakar3
1A. Nirmala, Research scholar, Department of computer science Annamalai University, Chidambaram, India.
2Dr. S. Arivalagan, Assistant Professor, Department of computer science and engineering, Annamalai University Chidambaram, India.
3Dr. P. Sudhakar, Assistant Professor, Department of computer science and engineering, Annamalai University Chidambaram, India.
Manuscript received on 8 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 2319-2327 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3852098319/19©BEIESP | DOI: 10.35940/ijrte.C3852.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, anomaly detection becomes a fascinating research application which usually raises an alarm in scenarios where the event varies from the actual event. Anomaly detection can be treated as a coarse-level video understanding problem that determines the existence of anomalies from habitual events. This paper introduces a new anomaly detection model by the use of Mask region based convolution neural network (R-CNN). The application of mask in the detection process helps to precisely identify the presence of anomalies in the scene. The effectiveness of the Mask R-CNN based anomaly detection model is validated against UCSD anomaly detection dataset. An extensive quantitative and experimental outcome evidently shows the superior nature of the presented model over the compared methods in a significant manner.
Keywords: Anomaly Detection; Deep Learning; RCNN; Object Identification.
Scope of the Article: Autonomic Computing and Agent-Based Systems