Rizky Fitria Haya, Kahlil Muchtar, Muhammad Subianto
The increasing complexity of urban public spaces, such as the Malioboro pedestrian area in Yogyakarta, requires more intelligent and adaptive crowd monitoring systems than conventional manual CCTV supervision. This paper proposes an end-to-end deep learning system for automatic group activity recognition from video. The system integrates YOLOv8 for pedestrian detection and tracking, 3D ResNet-18 for individual human activity recognition, and a 1D CNN with temporal attention for group-level inference based on activity ratio sequences. Experimental results show that the human activity recognition model achieves 93% accuracy with a 92% average F1-score, while the group activity model reaches 99.03% accuracy and a 99.03% Macro F1-score, outperforming GRU, TCN, and LSTM baselines. Evaluation under real-world conditions indicates that the system can adapt to varying lighting and crowd densities, although performance degrades under extreme conditions due to cascading errors from the individual recognition stage. These findings demonstrate the system’s potential for real-time collective behavior understanding, supporting automated crowd monitoring and sustainable urban area management. ©2025 IEEE.
Department of Informatics, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia
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