Learning Scales from Points
Zhiheng Ma, Xing Wei, Xiaopeng Hong, Yihong Gong
Abstract
Counting people automatically through computer vision technology is a challenging task. Recently, convolution neural network (CNN) based methods have made significant progress. Nonetheless, large scale variations of instances caused by, for example, perspective effects remain unsolved. Moreover, it is problematic to estimate scales with only point annotations. In this paper, we propose a scale-aware probabilistic model to handle this problem. Unlike previous methods that generate a single density map where instances of various scales are processed indiscriminately, we propose a density pyramid network (DPN), where each pyramid level handles instances within a particular scale range. Furthermore, we propose a scale distribution estimator (SDE) to learn scales of people from input data, under the weak supervision of point annotations. Finally, we adopt an instance-level probabilistic scale-aware model (IPSM) to guide the multi-scale training of DPN explicitly. Qualitative and quantitative experimental results demonstrate the effectiveness of the proposed method, which achieves competitive results on four widely used benchmarks.