Crowded Pedestrian Detection

Crowded pedestrian detection aims to accurately identify and track individuals within densely packed scenes, a challenging task due to significant object occlusion and overlapping. Current research focuses on improving the performance of deep learning models, particularly transformer-based architectures like DETR, by addressing issues such as adaptive query generation, more effective sample selection strategies, and robust anchor assignment methods to mitigate false positives. These advancements are crucial for applications ranging from public safety and crowd management to autonomous navigation in urban environments, improving the reliability and accuracy of automated systems operating in complex, high-density scenarios.