基于视觉异常检测的改进鸡群优化算法在视障人群监控视频中的应用

IF 1.7 Q2 REHABILITATION Scandinavian Journal of Disability Research Pub Date : 2023-01-01 DOI:10.57197/jdr-2023-0024
Hadeel Alsolai, F. Al-Wesabi, Abdelwahed Motwakel, Suhanda Drar
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引用次数: 0

摘要

深度学习技术已被有效地用于帮助视障人士完成不同的任务和提高整体可及性。设计一种专门针对视障人群的基于视觉的监控视频异常检测方法,可以大大提高人们的意识和安全性。虽然这是一个复杂的过程,但利用机器学习和计算机视觉算法构建一个系统是有潜力的。由于异常定义的不确定性,监控视频中的异常检测是一个繁琐的过程。在复杂的监控场景中,异常事件的类型可能共存且数量众多,如长期异常活动、物体运动和外观异常等。传统的视频异常检测技术无法识别这类异常动作。针对视障人群监控视频技术,设计了一种基于视觉异常检测(ICSO-VBAD)的改进鸡群优化器。ICSO-VBAD技术的目的是识别和分类异常的发生,以帮助视障人士。为了获得这一点,ICSO-VBAD技术利用effentnet模型来生成特征向量集合。在ICSO- vbad技术中,利用ICSO算法对EfficientNet模型进行超参数调优。采用自适应神经模糊推理系统模型对异常进行识别和分类。在基准数据集上对ICSO-VBAD系统的仿真结果进行了测试,结果指出了ICSO-VBAD技术在不同度量方面的改进。
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Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection on Surveillance Videos for Visually Challenged People
Deep learning technique has been efficiently used for assisting visually impaired people in different tasks and enhancing total accessibility. Designing a vision-based anomaly detection method on surveillance video specially developed for visually challenged people could considerably optimize awareness and safety. While it is a complex process, there is potential to construct a system by leveraging machine learning and computer vision algorithms. Anomaly detection in surveillance video is a tedious process because of the uncertain definition of abnormality. In the complicated surveillance scenario, the types of abnormal events might co-exist and are numerous, like long-term abnormal activities, motion and appearance anomaly of objects, etc. Conventional video anomaly detection techniques could not identify this kind of abnormal action. This study designs an Improved Chicken Swarm Optimizer with Vision-based Anomaly Detection (ICSO-VBAD) on surveillance videos technique for visually challenged people. The purpose of the ICSO-VBAD technique is to identify and classify the occurrence of anomalies for assisting visually challenged people. To obtain this, the ICSO-VBAD technique utilizes the EfficientNet model to produce a collection of feature vectors. In the ICSO-VBAD technique, the ICSO algorithm was exploited for the hyperparameter tuning of the EfficientNet model. For the identification and classification of anomalies, the adaptive neuro fuzzy inference system model was utilized. The simulation outcome of the ICSO-VBAD system was tested on benchmark datasets and the results pointed out the improvements of the ICSO-VBAD technique compared to recent approaches with respect to different measures.
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CiteScore
3.20
自引率
0.00%
发文量
13
审稿时长
16 weeks
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