• Ms. Insha Shaikh
• Dr. Imran Baig Mirza

Emergent behaviors such as panic, aggression, and dispersion in dynamic crowds pose significant challenges for real-time surveillance and public safety systems. Traditional single-model deep learning approaches often struggle with the variability and ambiguity inherent in such complex group dynamics. This paper presents a theoretical exploration of ensemble deep learning architectures designed to enhance the recognition of emergent behaviors in dynamic crowd environments. By leveraging multi-model intelligence, ensemble frameworks can integrate diverse learning perspectives, increase robustness to noise, and better generalize across varied scenarios. We discuss the foundational principles of ensemble learning, theoretical benefits over single-model approaches, and the design considerations essential for modeling temporal-spatial patterns in crowd behaviors. The paper concludes with open research challenges and future directions in applying ensemble deep learning for behavioral pattern recognition.

A Theoretical Perspective on Deep Ensemble Design for Emergent Behavior Recognition in Dynamic Crowds

"A Theoretical Perspective on Deep Ensemble Design for Emergent Behavior Recognition in Dynamic Crowds", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.g847-g851, November 2024, Available :http://www.jetir.org/papers/JETIR2411689.pdf

"A Theoretical Perspective on Deep Ensemble Design for Emergent Behavior Recognition in Dynamic Crowds", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppg847-g851, November 2024, Available at : http://www.jetir.org/papers/JETIR2411689.pdf