- Siyu Huang, Xi Li, Zhi-Qi Cheng, Zhongfei Zhang, Fei Wu, Alexander Hauptmann. 2018. Crowd Counting with Stacked Pooling for Boosting Scale Invariance. Abstract: —In this work, we take insight into the crowd counting problem by exploring the phenomenon of cross-scale visual similarity caused by perspective distortions. It is a quite common phenomenon in crowd scenarios, suggesting the crowd counting model to enable a good performance of scale invariance. Existing deep crowd counting approaches mainly focus on the multi-scale techniques over convolutional layers to capture scale-adaptive features, resulting in high computing costs. In this paper, we propose simple but effective pooling variants, i.e., multi-kernel pooling and stacked pooling, to take place of the vanilla pooling layers in convolutional neural networks (CNNs) for boosting the scale invariance. Speciﬁcally, the multi-kernel pooling comprises of pooling kernels with multiple receptive ﬁelds to capture the responses at multi-scale local ranges. The stacked pooling is an equivalent form of multi-kernel pooling, while it reduces considerable computing cost. Our proposed pooling modules do not introduce extra parameters and can be easily implemented in practice. Empirical studies on two benchmark crowd counting datasets show that the proposed pooling modules beat the vanilla pooling layer in most experimental cases.
