Residual Learning Ensemble with Bi-LSTM for Object Motion Recognition

In this paper, we propose a novel approach for Object Motion Recognition using a Residual Learning Ensemble of Bi-directional Long Short-Term Memory (Bi-LSTM) networks. The goal of the proposed method is to effectively capture and classify the dynamic behavior of moving objects within video sequences or sensor data. Traditional motion recognition methods often face challenges in handling the complexities of object motion due to occlusions, varying velocities, and non-linear patterns. Our approach leverages the power of residual learning to improve the training process and mitigate the vanishing gradient problem, enhancing the model's ability to capture long-term dependencies in sequential data. By employing an ensemble of Bi-LSTMs, we combine multiple models with diverse perspectives, enabling more robust motion feature extraction and classification. The residual connections facilitate efficient learning by allowing the network to focus on learning the residuals or changes in motion, rather than the entire input, leading to improved recognition accuracy. We evaluate our method on standard motion recognition datasets, demonstrating superior performance compared to existing approaches. The results highlight the efficacy of the residual learning ensemble Bi-LSTM model in recognizing complex object motion patterns, providing a promising solution for real-time object motion tracking and recognition applications in robotics, autonomous systems, and surveillance.