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Anomaly Detection in Behavioral Surveillance Using Deep Learning Techniques
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Author(s): Arun Agrawal (Institute of Technology and Management, Gwalior, India), Rashmi S. Bhaskar (BNMIT Bengaluru, India), Sanjay Badhe (D.Y. Patil International University, Akurdi, India), Suvarna Kisan Thakur (Dr. Babasaheb Ambedkar Technological University, India), Anika Bhandari (Chandigarh University, India), Kajal Jain (Maharishi Markandeshwar University, Mullana, India)and Deepak Gupta (Institute of Technology and Management, Gwalior, India)
Copyright: 2026
Pages: 36
Source title:
AI-Based Data Mobility and Intelligent Modeling for Smart Cities
Source Author(s)/Editor(s): Sultan Ahmad (Prince Sattam Bin Abdulaziz University, Saudi Arabia), Sudan Jha (Kathmandu University, Nepal)and Md Alimul Haque (Veer Kunwar Singh University, India)
DOI: 10.4018/979-8-3373-4202-3.ch009
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Abstract
This chapter explores techniques for detecting abnormal behavior in surveillance systems using deep learning. Modern surveillance relies on advanced models to identify suspicious activities crucial for safety and monitoring. The discussion traces the evolution from traditional rule-based methods to deep models such as convolutional and recurrent neural networks, autoencoders, and transformers. It highlights challenges in defining normality, managing temporal variations, handling large-scale data, and ensuring privacy. The chapter examines temporal behavior models, including LSTM encoders, 3D CNNs, and attention-based architectures. Real-world applications in video surveillance, crowd monitoring, and human activity analysis are reviewed. Issues like data imbalance, real-time inference, model explainability, and ethics are discussed. Finally, performance metrics, evaluation protocols, and model comparisons are outlined to guide readers in designing effective anomaly detection frameworks for practical use.
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