Trajectory Encoder

Trajectory encoding focuses on representing the movement of objects or agents over time as meaningful data for various applications. Current research heavily utilizes transformer networks and recurrent neural networks (RNNs), often incorporating techniques like masked autoencoders and mutual distillation to improve feature extraction and prediction accuracy. These advancements are driving progress in diverse fields, including autonomous driving (for motion prediction and object detection), and human behavior analysis (for understanding actions and linking trajectories to individuals). The resulting improved representations are crucial for enhancing the performance of systems relying on temporal data.