  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.
