Associated metric coding network for pedestrian detection

Convolutional neural networks (CNNs) have played a significant role in pedestrian detection, owing to their capacity of learning deep features from original image. It is noteworthy that most of the existing generalized objection detection networks must crop or warp the inputs to fixed-size which leads to the low performance on multifarious input sizes. Moreover, the lacking of hard negatives mining constrains the ability of recognition. To alleviate the problems, an associated work network which contains a metric coding net (MC-net) and a weighted association CNN (WA-CNN), is introduced. With region proposal net in low layer, MC-net is introduced to strengthen the difference of intra-class. WA-CNN can be regarded as a network to reinforce the distance of inter-class and it associates the MC-net to accomplish the detection task by a weighted strategy. Extensive evaluations show that our approach outperforms the state-of-the-art methods on the Caltech and INRIA datasets.