Real Time Tracking

Real-time tracking aims to identify and follow objects' trajectories in video or sensor data with minimal latency, crucial for applications like autonomous driving and surveillance. Current research emphasizes improving accuracy and speed through novel algorithms, including those based on continual learning, hybrid attention mechanisms in deep learning models, and efficient data structures like tree-based indexing for label retrieval. These advancements are driving progress in various fields, enabling more robust and efficient object tracking in complex, real-world scenarios.