Scale-aware hierarchical loss: A multipath RPN for multi-scale pedestrian detection

Pedestrians with different spatial scales exhibiting dramatically differences, the serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection. Considering the local feature differences for multi-scale pedestrians, a scale-aware multipath region proposal network is exploited to improve the recall rate, which is divided into several branches to generate a proper object proposal for target with specific scale range. Moreover, motivated by the visual semantic concepts of different convolutional layers, a scale-aware hierarchical loss model is introduced to minimize the error rate for pedestrians with different scales, in which the hierarchical features of higher convolutional layers are jointed to calculate a multi-task loss to learn scale-aware weighting of multipath region proposal network for each object proposal. Finally, compared to state-of-the-art methods, experimental results on the challenging ETH and Caltech benchmark show the superiority of the proposed method for large variance in instance scales.