  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.
