{"title":"Leveraging YOLOv5s with optimization‐based effective anomaly detection in pedestrian walkways","authors":"Allabaksh Shaik, Shaik Mahaboob Basha","doi":"10.1111/exsy.13640","DOIUrl":null,"url":null,"abstract":"Currently, video surveillance is generally used to safeguard safety in public places like railway stations, traffic signals, malls, and so on. Video anomaly recognition and localization are the main components of the intelligent video surveillance method. Video anomaly recognition refers to the procedure of spatiotemporal localization of the abnormal design existing in the video. A main task in video surveillance is the classification of anomalies that occur in it like thefts, crimes, and so forth. Also, anomaly recognition in pedestrian walkways has enlarged major attention among the computer vision (CV) groups to improve pedestrian protection. The current developments in Deep Learning (DL) methods have great attention to dissimilar procedures like image classification, object recognition, and so forth. This study designs an Optimal Deep Learning for Effective Anomaly Detection in Pedestrian Walkways (ODL‐EADPW) model. The ODL‐EADPW technique employs a fine‐tuned DL model for the identification of pedestrians and anomalies in the walkways. In the ODL‐EADPW technique, the image pre‐processing is primarily involved in two stages median filtering (MF) based noise removal and adaptive histogram equalization (AHE)‐based contrast enhancement. For anomaly detection in pedestrian walkways, the ODL‐EADPW technique uses the YOLOv5s model with EfficientRep as a backbone network. To enhance the detection results of the ODL‐EADPW technique, a stochastic gradient descent (SGD) optimizer was employed to perfect the hyperparameters of the EfficientRep model. The performance evaluation of the ODL‐EADPW methodology is implemented on the UCSD Anomaly detection dataset. An extensive comparison study stated that the ODL‐EADPW technique gains effectual detection results over other DL models in terms of different measures.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1111/exsy.13640","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Currently, video surveillance is generally used to safeguard safety in public places like railway stations, traffic signals, malls, and so on. Video anomaly recognition and localization are the main components of the intelligent video surveillance method. Video anomaly recognition refers to the procedure of spatiotemporal localization of the abnormal design existing in the video. A main task in video surveillance is the classification of anomalies that occur in it like thefts, crimes, and so forth. Also, anomaly recognition in pedestrian walkways has enlarged major attention among the computer vision (CV) groups to improve pedestrian protection. The current developments in Deep Learning (DL) methods have great attention to dissimilar procedures like image classification, object recognition, and so forth. This study designs an Optimal Deep Learning for Effective Anomaly Detection in Pedestrian Walkways (ODL‐EADPW) model. The ODL‐EADPW technique employs a fine‐tuned DL model for the identification of pedestrians and anomalies in the walkways. In the ODL‐EADPW technique, the image pre‐processing is primarily involved in two stages median filtering (MF) based noise removal and adaptive histogram equalization (AHE)‐based contrast enhancement. For anomaly detection in pedestrian walkways, the ODL‐EADPW technique uses the YOLOv5s model with EfficientRep as a backbone network. To enhance the detection results of the ODL‐EADPW technique, a stochastic gradient descent (SGD) optimizer was employed to perfect the hyperparameters of the EfficientRep model. The performance evaluation of the ODL‐EADPW methodology is implemented on the UCSD Anomaly detection dataset. An extensive comparison study stated that the ODL‐EADPW technique gains effectual detection results over other DL models in terms of different measures.
期刊介绍:
Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper.
As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.