Deep Learning-Based Human Activity Recognition Using Dilated CNN and LSTM on Video Sequences of Various Actions Dataset

Human Activity Recognition (HAR) plays a critical role across various fields, including surveillance, healthcare, and robotics, by enabling systems to interpret and respond to human behaviors. In this research, we present an innovative method for HAR that leverages the strengths of Dilated Convolutional Neural Networks (CNNs) integrated with Long Short-Term Memory (LSTM) networks. The proposed architecture achieves an impressive accuracy of 94.9%, surpassing the conventional CNN-LSTM approach, which achieves 93.7% accuracy on the challenging UCF 50 dataset. The use of dilated CNNs significantly enhances the model's ability to capture extensive spatial-temporal features by expanding the receptive field, thus enabling the recognition of intricate human activities. This approach effectively preserves fine-grained details without increasing computational costs. The inclusion of LSTM layers further strengthens the model's performance by capturing temporal dependencies, allowing for a deeper understanding of action sequences over time. To validate the robustness of our model, we assessed its generalization capabilities on an unseen YouTube video, demonstrating its adaptability to real-world applications. The superior performance and flexibility of our approach suggests its potential to advance HAR applications in areas like surveillance, human-computer interaction, and healthcare monitoring.