Pedestrian Detection With Dilated Convolution, Region Proposal Network And Boosted Decision Trees

With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different receptive field scales, we concat the features of different layers with the dilated convolutional features to get the final features. Finally, the candidate boxes are sent to the boosted decision trees to be classified using the scores and features. We evaluated our method on the Caltech Pedestrian Detection Benchmark. Comparing with other state-of-the-art detection methods, the proposed framework with dilated convolution has better performance.