A novel hybrid architecture for video frame prediction: combining convolutional LSTM and 3D CNN
摘要
Video frame prediction represents a fundamental challenge in computer vision, necessitating precise modeling of both spatial and temporal dynamics within video sequences. This computational task holds substantial implications across diverse domains, including video compression optimization, robust object tracking systems, and advanced motion forecasting applications. In this investigation, we present a novel hybrid architecture that synthesizes the complementary strengths of Convolutional Long Short-Term Memory (ConvLSTM) networks and three-dimensional Convolutional Neural Networks (3D CNN) for enhanced frame prediction capabilities. Our methodological framework incorporates a ConvLSTM component that fundamentally augments the traditional LSTM architecture through the integration of convolutional operations, thereby facilitating sophisticated modeling of sequential dependencies. Concurrently, the 3D CNN component employs volumetric convolutional layers to extract rich spatio-temporal features from the input sequences. Rigorous empirical evaluation demonstrates the superior performance of the ConvLSTM architecture, which consistently yields reduced validation errors and elevated coefficients of determination. Specifically, the ConvLSTM model achieves a validation Mean Squared Error (MSE) of 0.0237 and an