Real-time pedestrian detection for driver assistance systems based on deep learning

Abstract

We present a model for real-time pedestrian detection based on a deep learning framework. With respect to the base network for feature extraction, we have improved the network based on Mobilenet which is a simple and fast convolutional neural network. We only use the front part of its network and then build several new multi-scale convolutional layers to calculate multi-scale feature maps. With respect to the detection network behind the feature extraction, we use a simplified SSD(single shot multibox detector) model to detect pedestrians with fewer feature maps. In addition, we design detection boxes with specific sizes according to pedestrian’s shape characteristics. To avoid overfitting, we apply data augmentation and dropout techniques to training. Experimental results on PASCAL VOC and KITTI confirm that the speed of our detection model has been increased by 22.2% while precision remains almost unchanged. Our approach makes a trade-off between speed and precision, and has an obvious speed advantage over other detection approaches.