Adding depth information significantly improves object counting in crowded scenes—the method reduces counting errors by 62% compared to RGB-only approaches, showing that 3D spatial cues are crucial for handling occlusions.
This paper tackles counting objects in crowded, occluded video scenes by combining RGB images with depth information. The method uses a depth-guided detector with cross-attention between color and depth data, plus occlusion prediction, to better identify individual objects even when they overlap. The authors also release a new RGB-D dataset for this task.