Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting

Abstract

Due to a wide range of various application scenes, robust crowd counting is still quite difficult and the performance is far from being satisfied. In this paper, we propose a novel robust crowd counting method by introducing a weakly supervised crowd-wise attention network. The proposed work improves the counting accuracy and robustness by: i) Weakly-supervised crowd segmentation. With a generated segmentation label using motion-guided region-growth, both the appearance feature of one-labeled image and motion features abstracted from its adjacent unlabeled frames, are combined to implement weakly supervised crowd region segmentation, with which active crowd region can be finely perceived from different background disturbances. ii) More accurate spatial attention. We generate a spatial attention map based on the active crowd segmentation, which is used to reweigh the appearance feature to achieve attention-based density estimation. Evaluation of the widely used World Expo' 10 dataset shows that the proposed work can achieve state-of-the-art performance on both accuracy and robustness.