Real-Time Video Object Detection and Trajectory Prediction Using Deep Convolutional Neural Networks and Long Short-Term Memory Networks

Real-time object detection and trajectory prediction are crucial tasks in intelligent video systems, particularly for applications such as autonomous driving and urban surveillance. Traditional methods often treat these tasks separately, resulting in suboptimal performance in dynamic environments. To address this, we propose a unified framework combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks within a multi-task learning architecture. Our model simultaneously performs object detection and trajectory prediction by sharing feature representations and applying joint optimization. Experiments on a real-world traffic dataset demonstrate significant improvements in both detection accuracy and trajectory prediction, while maintaining real-time inference. This work offers a comprehensive solution to integrating detection and prediction tasks, with implications for the deployment of deep learning models in safety-critical systems.