Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Shaik, Allabaksha; b; * | Basha, Shaik Mahaboobc
Affiliations: [a] Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India | [b] Sri Venkateswara College of Engineering Tirupati, Affiliated to Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India | [c] N.B.K.R. Institute of Science and Technology, Vidyanagar, Affiliated to Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India
Correspondence: [*] Corresponding author: Allabaksh Shaik, Research Scholar, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India. Tel.: +91 9700644100; E-mail: baksh402@gmail.com.
Abstract: Anomaly detection is a branch of behavior understanding in surveillance scenes, where anomalies represent a deviation in the behavior of scene entities (viz.,humans, vehicles, and environment) from regular patterns. In pedestrian walkways, this plays a vital role in enhancing safety. With the widespread use of video surveillance systems and the escalating video volume, manual examination of abnormal events becomes time-intensive.Hence, the need for an automated surveillance system adept at anomaly detection is crucial, especially within the realm of computer vision (CV) research. The surge in interest towards deep learning (DL) algorithms has significantly impacted CV techniques, including object detection and classification. Unlike traditional reliance on supervised learning requiring labeled datasets, DL offers advancements in these applications. Thus, this study presents an Optimal Deep Transfer Learning Enabled Object Detector for Anomaly Recognition in Pedestrian Ways (ODTLOD-ARPW) technique. The purpose of the ODTLOD-ARPW method is to recognize the occurrence of anomalies in pedestrian walkways using a DL-based object detector. In the ODTLOD-ARPW technique, the image pre-processing initially takes place using two sub-processes namely Wiener filtering (WF) based pre-processing and dynamic histogram equalization-based contrast enhancement. For anomaly detection, the ODTLOD-ARPW technique employs the YOLOV8s model which offers enhanced accuracy and performance. The hyperparameter tuning process takes place using a root mean square propagation (RMSProp) optimizer. The performance analysis of the ODTLOD-ARPW method is tested under the UCSD anomaly detection dataset. An extensive comparative study reported that the ODTLOD-ARPW technique reaches an effective performance with other models with maximum accuracy of 98.67%.
Keywords: Anomaly detection, pedestrian ways, hyperparameter tuning, deep learning, wiener filtering
DOI: 10.3233/IDT-240040
Journal: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1123-1138, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl