Abstract
This paper showcases an experimental study on anomaly detection using computer vision. The study focuses on class distinction and performance evaluation, combining OpenCV with deep learning techniques while employing a TensorFlow-based convolutional neural network for real-time face recognition and classification. The system effectively distinguishes among three classes: authorized personnel (admin), intruders, and non-human entities. A MobileNetV2-based deep learning model is utilized to optimize real-time performance, ensuring high computational efficiency without compromising accuracy. Extensive dataset preprocessing, including image augmentation and normalization, enhances the models generalization capabilities. Our analysis demonstrates classification accuracies of 90.20% for admin, 98.60% for intruders, and 75.80% for non-human detection, while maintaining an average processing rate of 30 frames per second. The study leverages transfer learning, batch normalization, and Adam optimization to achieve stable and robust learning, and a comparative analysis of class differentiation strategies highlights the impact of feature extraction techniques and training methodologies. The results indicate that advanced feature selection and data augmentation significantly enhance detection performance, particularly in distinguishing human from non-human scenes. As an experimental study, this research provides critical insights into optimizing deep learning-based surveillance systems for high-security environments and improving the accuracy and efficiency of real-time anomaly detection.
Abstract (translated)
这篇论文展示了一项使用计算机视觉进行异常检测的实验研究。该研究侧重于类别区分和性能评估，结合了OpenCV与深度学习技术，并采用基于TensorFlow的卷积神经网络进行实时面部识别和分类。系统能够有效地区分三种类别：授权人员（管理员）、入侵者和非人类实体。为了优化实时表现，使用了一个基于MobileNetV2的深度学习模型，在保证高精度的同时实现了高效的计算效率。通过对大量数据集进行预处理，包括图像增强和标准化，提升了模型的泛化能力。我们的分析显示，对于管理员、入侵者以及非人类实体检测的分类准确率分别达到了90.20%、98.60% 和75.80%，同时保持了每秒30帧的平均处理速率。该研究通过利用迁移学习、批量归一化和Adam优化器实现了稳定且稳健的学习效果，并对类别区分策略进行了比较分析，强调了特征提取技术和训练方法的影响。结果显示，高级别特征选择和数据增强显著提高了检测性能，尤其是在区分人类与非人类场景方面。作为一项实验性研究，本研究为在高安全环境中优化基于深度学习的监控系统提供了关键见解，并提升了实时异常检测的准确性和效率。
URL
https://arxiv.org/abs/2503.19100
