Intelligent Human Behaviour Detection Leveraging ConvLSTM and LRCN Deep Learning Approaches

This research delves into intelligent human behavior detection from video sequences by employing advanced deep learning methodologies. The study harnesses the capabilities of Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Networks (LRCN) architectures, which are adept at capturing both temporal and spatial dynamics in video data. Using the extensive Kinetics dataset, which includes a wide range of human activities and contexts, the video data was converted into sequential image frames for model training. The ConvLSTM and LRCN models were implemented and trained with the TensorFlow/Keras framework on GPU-accelerated systems. During training, both models exhibited effective learning, with improved accuracy and reduced loss. However, validation metrics revealed fluctuations, indicating possible overfitting and challenges in generalizing to new data. To address these issues, regularization techniques such as dropout and early stopping were applied, alongside comprehensive hyperparameter tuning. Additionally, the study investigated the use of transfer learning to enhance model performance. This research lays the groundwork for sophisticated video-based human behavior detection systems, with applications in surveillance, healthcare, and human-computer interaction. Future work will aim to bolster model robustness and broaden the scope of action recognition.