AI Enhanced Surveillance for Identifying and Recognizing Crowd Behavior

Abstract

The application of deep learning methods in AI-powered surveillance systems for detecting and interpreting crowd behavior.It reviews the latest progress in computer vision and neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers—essential for tasks such as estimating crowd density, recognizing activities, and detecting anomalies. The discussion covers both supervised and unsupervised learning strategies, domain adaptation through transfer learning, and the integration of multiple data sources to enhance accuracy and system resilience.It also emphasizes the impact of hardware advancements, like GPUs and edge computing, in enabling real-time analysis.Furthermore, the paper addresses key challenges, including limited data availability, lack of model transparency, and inherent biases in crowd behavior datasets.This comprehensive survey aims to deepen understanding in the field and support the development of more efficient, reliable, and ethically sound crowd monitoring solutions

Introduction

The text discusses the vital role of deep learning, a subset of machine learning under artificial intelligence (AI), in crowd surveillance and behavior analysis. Deep learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can accurately detect, monitor, and predict abnormal behaviors in real-time crowds. These systems are essential for ensuring public safety in densely populated environments like stadiums, malls, and events, where risks such as stampedes or riots are high.

Traditional surveillance systems, like CCTV, lack scalability, real-time analysis, and the ability to detect subtle or emerging crowd behaviors. In contrast, AI-powered systems analyze real-time video inputs, identify suspicious actions (e.g., panic, aggression), and use predictive analytics to prevent incidents. Techniques such as optical flow analysis and skeleton tracking enhance these systems’ accuracy and responsiveness.

Literature Survey Highlights

1. ACSAM (Wu) – CNN-based system that detects abnormal group behaviors with high precision, outperforming prior methods in accuracy and recall.

2. Zaidi – Enhanced surveillance via time-distributed CNN and Conv3D models, achieving high accuracy in suspicious activity detection.

3. Kuppusamy – 3D CNNs improve real-time abnormal behavior detection compared to manual video review.

4. Chen – Edge-based smart surveillance reduces cloud dependency for real-time object detection in smart cities.

5. Khan – AI with big data analytics used in urban surveillance for trend analysis and predictive modeling.

6. Jadhav – LSTM and Fully Convolutional Networks (FCNs) automate suspicious behavior detection, minimizing human error.

7. Hussein – Employed IoT technologies (RFID, Wi-Fi) to predict crowd movements and detect potential stampedes.

8. General Survey – Reviews traditional and deep learning methods for structured and unstructured crowd monitoring.

9. Singh – Combined computer vision and ML (SVM, HOG, ViF) in autonomous surveillance for real-time anomaly detection.

10. Al-Shaery – AI-powered video analysis used in commercial surveillance (e.g., customer behavior, re-identification).

11. Vishakha L. – Defines core crowd analysis concepts: dynamics, density, motion, behavior analysis, and CNN/RNN integration.

12. Kim & Kim – Real-time detection system using pedestrian tracking and behavior analysis, showing strong results with CCTV data.

13. Shetty – Developed a Raspberry Pi-based crowd control system using Haar cascade classifiers and optical flow.

14. Ganga – Surveyed object detection in deep learning (e.g., YOLO, SSD), showing its strength in real-time crowd analysis.

15. Fadzil – Evaluated crowd monitoring systems during COVID-19 using thermal imaging, video analysis, and smart surveillance integration.

Related Work

• Surveillance Systems: Evolved from manual CCTV review to AI-powered, real-time analysis systems.

• Video Analysis: Advanced with CNNs for spatial detection, RNNs for temporal analysis, and C3D for spatio-temporal understanding.

• Challenges: Real-time high-res processing, handling occlusions, detecting subtle behaviors, and adapting to varied crowd types.

Conclusion

The transformative impact of deep learning and AI driven surveillance systems on public safety, urban security, and commercial applications.By leveraging advanced architectures like Convolutional Neural Networks (cnns), Fully Convolutional Networks (fcns), and Long Short-Term Memory (LSTM) models, these systems effectively detect abnormal behaviors and suspicious activities in real time.This research highlights the superiority of AI methods over traditional techniques, particularly in handling dense crowds, occlusions, and unorganized crowd scenarios.Moreover, edge computing and big data analytics enhance system responsiveness and provide predictive insights while addressing latency and data transmission challenges.However, the studies also underscore the importance of balancing security with privacy and legal compliance.As AI models and computational resources continue to evolve, these technologies will become increasingly vital for efficient surveillance, crowd management, and customer behavior analysis, setting a benchmark for future advancements in intelligent monitoring systems. The system demonstrates the potential of deep learning to accurately classify a range of crowd behaviors, including natural behavior, fights, peaceful gatherings, violent gatherings, and weapon detection.Future work will focus on: Improving the accuracy and robustness of the behavior recognition models.Exploring methods for predicting crowd behavior to enable proactive intervention.

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Copyright

Copyright © 2025 B. S. Prakash, B. Tamilsudar, T. Viswanath Kani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.