Learning Independent Instance Maps for Crowd Localization
This work provides a more accurate method for localizing individual heads in extremely dense and scale-variant crowd scenes, which is crucial for applications in crowd analysis and management.
This paper addresses the problem of accurately locating individual heads in crowded scenes, proposing a novel Independent Instance Map segmentation (IIM) framework. The method segments crowds into non-overlapping, independent connected components to determine head positions and counts. It achieves a significant improvement of 10.4% in F1-measure on the NWPU-Crowd Localization task.
Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse prediction, and segmentation/detection-based methods cannot handle extremely dense scenes and large-range scale-variations crowds. To this end, we propose an end-to-end and straightforward framework for crowd localization, named Independent Instance Map segmentation (IIM). Different from density maps and boxes regression, each instance in IIM is non-overlapped. By segmenting crowds into independent connected components, the positions and the crowd counts (the centers and the number of components, respectively) are obtained. Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps. BM brings two advantages into localization models: 1) adaptively learn a threshold map for different images to detect each instance more accurately; 2) directly train the model using loss on binary predictions and labels. Extensive experiments verify the proposed method is effective and outperforms the-state-of-the-art methods on the five popular crowd datasets. Significantly, IIM improves F1-measure by 10.4% on the NWPU-Crowd Localization task. The source code and pre-trained models will be released at https://github.com/taohan10200/IIM.