Crowded Environment

Research on crowded environments focuses on understanding and managing the complexities of human and robot interactions within densely populated spaces. Current efforts concentrate on developing robust algorithms for tasks such as crowd counting, trajectory prediction, and socially compliant robot navigation, often employing deep reinforcement learning, large language models, and model predictive control. These advancements are crucial for improving safety, efficiency, and social interaction in various applications, ranging from urban planning and security to robotics and human-computer interaction. The development of large, diverse datasets, such as panoptic segmentation and tracking benchmarks, is also a key area of focus, enabling more rigorous evaluation and comparison of different approaches.