CSANet: Channel and Spatial Mixed Attention CNN for Pedestrian Detection

Current mainstream pedestrian detectors tend to profit directly from convolutional neural networks (CNNs) that are designed for image classification. While requiring a large downsampling factor to produce high-level semantic features, CNNs cannot adaptively focus on the useful channels and regions of the feature maps, which limits the accuracy of pedestrian detection. To obtain a higher accuracy, we propose a single-stage pedestrian detector with channel and spatial attentions (CSANet), which can locate useful channels and regions automatically while extracting features. The backbone of CSANet is different from that of mainstream pedestrian detectors, which can effectively highlight the pedestrian-likely regions and suppress the background. Specifically, we model contextual dependencies from channel and spatial dimensions of the feature maps, respectively. The channel attention module can selectively promote CNNs to focus on key channels by integrating associated features. Meantime, the spatial attention module can illuminate semantic pixels by aggregating similar features of all channels. Eventually, the two modules are connected in series to further enhance the representation of feature maps. Experiment results show that CSANet achieves the state-of-the-art performance with $MR^{-2}$ of 3.55% on Caltech dataset and obtains competitive performance on CityPersons dataset while maintaining a high computational efficiency.