Khanam Shaista, Sharif Muhammad, Raza Mudassar, Ishaq Waqar, Fayyaz Muhammad, Kadry Seifedine
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Punjab, Pakistan.
Department of Computer Science, Namal University, Mianwali, Pakistan.
PLoS One. 2025 May 19;20(5):e0313692. doi: 10.1371/journal.pone.0313692. eCollection 2025.
Surveillance systems are integral to ensuring public safety by detecting unusual incidents, yet existing methods often struggle with accuracy and robustness. This study introduces an advanced framework for anomaly recognition in surveillance, leveraging deep learning to address these challenges and achieve significant improvements over current techniques. The framework begins with preprocessing input images using histogram equalization to enhance feature visibility. It then employs two DCNNs for feature extraction: a novel 63-layer CNN, "Up-to-the-Minute-Net," and the established Inception-Resnet-v2. The features extracted by both models are fused and optimized through two sophisticated feature selection techniques: Dragonfly and Genetic Algorithm (GA). The optimization process involves rigorous experimentation with 5- and 10-fold cross-validation to evaluate performance across various feature sets. The proposed approach achieves an unprecedented 99.9% accuracy in 5-fold cross-validation using the GA optimizer with 2500 selected features, demonstrating a substantial leap in accuracy compared to existing methods. This study's contribution lies in its innovative combination of deep learning models and advanced feature optimization techniques, setting a new benchmark in the field of anomaly recognition for surveillance systems and showcasing the potential for practical real-world applications.
监控系统对于通过检测异常事件来确保公共安全至关重要，但现有方法在准确性和鲁棒性方面往往存在困难。本研究介绍了一种用于监控中异常识别的先进框架，利用深度学习来应对这些挑战，并在当前技术基础上实现显著改进。该框架首先使用直方图均衡化对输入图像进行预处理，以增强特征可见性。然后，它采用两个深度卷积神经网络（DCNN）进行特征提取：一个新颖的63层卷积神经网络“即时网络”和已有的Inception-Resnet-v2。通过两种复杂的特征选择技术——蜻蜓算法和遗传算法（GA），对这两个模型提取的特征进行融合和优化。优化过程涉及使用5折和10折交叉验证进行严格实验，以评估各种特征集的性能。所提出的方法在使用GA优化器和2500个选定特征的5折交叉验证中，达到了前所未有的99.9%的准确率，与现有方法相比，准确率有了大幅提升。本研究的贡献在于其将深度学习模型与先进的特征优化技术进行了创新结合，在监控系统异常识别领域树立了新的标杆，并展示了实际应用的潜力。
