Abstract
Pedestrian detection is an important application in computer vision. Due to uneven illumination, serious obstacles, low quality images, abnormal posture and other factors, pedestrian detection faces the problem of low detection accuracy in complex scenes. In this paper, pedestrian detection algorithm based on deep convolution neural network is studied. Since shorter connections between the input and output layers can help to build deeper and more efficient network in CNN, a densely connected convolution structure is introduced in this paper to optimize the Deconvolutional Single Shot Detector and improve the feature utilization and reduce the network parameters. Meanwhile, by augmenting the context information, the detection performance for small size pedestrians is improved. The initial experimental results show that the proposed algorithm improves the detection accuracy to 87.84% at the speed of 12.3fps on low-resolution (64x128) pedestrian dataset, which outperforms the reference algorithms.
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