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What is Suspicious Activity Detection with UCF-Crime about?

This paper introduces a hybrid deep learning model combining DenseNet, Temporal Convolutional Networks (TCN), and an attention mechanism for detecting suspicious activities in surveillance videos using the UCF-Crime dataset. The model processes 16-frame sequences and achieves approximately 85% validation accuracy after training on over 1.2 million frames, demonstrating potential for real-time applications. Future enhancements are suggested to improve robustness and efficiency, including video input support

Author

Rithanya S

Language

EN