Hierarchical Deep Learning Architectures for Multiclass Violation Detection in Urban Surveillance Systems Authors

Urban surveillance systems face the challenge of recognizing a wide spectrum of violations in real time under diverse environmental conditions. This study proposes a hierarchical deep learning architecture that integrates convolutional neural networks (CNNs) for spatial feature extraction and bidirectional recurrent neural networks (RNNs) for temporal sequence modeling. A large-scale dataset was constructed, comprising 120,000 labeled video segments across 172 violation categories, each annotated by three independent experts with an inter-annotator agreement of κ = 0.87. To ensure rigorous evaluation, the dataset was split into training (80%), validation (10%), and testing (10%) sets, and control baselines including CNN-only, RNN-only, and flat CNN-RNN models were established. Experimental results demonstrate that the proposed framework achieved a macro-averaged F1-score of 0.961, outperforming the baseline models by margins ranging from 12% to 17%. Robustness tests under illumination shifts, occlusion, and camera angle variations showed less than 3% degradation in accuracy, compared with more than 10% performance loss in baseline models. Furthermore, hierarchical classification reduced misclassification among semantically similar events, such as "illegal parking" and "lane obstruction," which were frequently confused in flat architectures. The scalability of the framework was confirmed by its ability to handle over 170 categories without performance decline, a capability rarely reported in prior studies. These findings indicate that hierarchical spatial-temporal modeling not only improves classification accuracy but also enhances robustness and scalability, making it a promising solution for intelligent urban surveillance. The proposed architecture offers practical significance for real-world deployment and provides methodological insights for the design of next-generation smart surveillance systems.

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