Multi-scale pedestrian detection using skip pooling and recurrent convolution

Detecting pedestrians of different scales is essential for applications like autonomous driving. Recent research progress showed that combining multiple feature maps and contextual information helps detecting objects of different scales. In this paper, we propose a multi-scale pedestrian detector that combines skip pooling from multi-resolution feature maps and recurrent convolutional layers for extracting contextual information. To fully exploit the unique characteristics of the features at different levels for multi-scale pedestrian detection, the multi-scale features and the context features are fused at the fully connected layer. To gather spatial contextual information, we propose a modified recurrent convolutional layer that produces context feature maps with different resolutions. In addition, we construct a set of scale-dependent classification and bounding box regression subnetworks to further improve the performance of multi-scale pedestrian detection. Experiments on Caltech and KITTI pedestrian detection benchmark datasets show that the proposed method achieves the state-of-the-art performance with faster speed.