  In this paper, we present an efficient pedestrian detection system, designed
by fusion of multiple deep neural network (DNN) systems. Pedestrian candidates
are first generated by a single shot convolutional multi-box detector at
different locations with various scales and aspect ratios. The candidate
generator is designed to provide the majority of ground truth pedestrian
annotations at the cost of a large number of false positives. Then, a
classification system using the idea of ensemble learning is deployed to
improve the detection accuracy. The classification system further classifies
the generated candidates based on opinions of multiple deep verification
networks and a fusion network which utilizes a novel soft-rejection fusion
method to adjust the confidence in the detection results. To improve the
training of the deep verification networks, a novel soft-label method is
devised to assign floating point labels to the generated pedestrian candidates.
A deep context aggregation semantic segmentation network also provides
pixel-level classification of the scene and its results are softly fused with
the detection results by the single shot detector. Our pedestrian detector
compared favorably to state-of-art methods on all popular pedestrian detection
datasets. For example, our fused DNN has better detection accuracy on the
Caltech Pedestrian dataset than all previous state of art methods, while also
being the fastest. We significantly improved the log-average miss rate on the
Caltech pedestrian dataset to 7.67% and achieved the new state-of-the-art.
