We propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for parallel processing of multiple networks for speed. A single shot deep convolutional network is trained as a object detector to generate all possible pedestrian candidates of different sizes and occlusions. This network outputs a large variety of pedestrian candidates to cover the majority of ground-truth pedestrians while also introducing a large number of false positives. Next, multiple deep neural networks are used in parallel for further refinement of these pedestrian candidates. We introduce a soft-rejection based network fusion method to fuse the soft metrics from all networks together to generate the final confidence scores. Our method performs better than existing state-of-the-arts, especially when detecting small-size and occluded pedestrians. Furthermore, we propose a method for integrating pixel-wise semantic segmentation network into the network fusion architecture as a reinforcement to the pedestrian detector. The approach outperforms state-of-the-art methods on most protocols on Caltech Pedestrian dataset, with significant boosts on several protocols. It is also faster than all other methods.
Du et al. REF proposed a Fused Deep Neural Network (F-DNN) to increase the variety of proposals, and utilize a soft-weighting scheme to combine multiple downstream classifier for further refinement.
Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection
2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science