A pedestrian detection algorithm based on deep deconvolution networks in complex scenes

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.

[1]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[3]  Wei Liu,et al.  DSSD : Deconvolutional Single Shot Detector , 2017, ArXiv.

[4]  In-So Kweon,et al.  AttentionNet: Aggregating Weak Directions for Accurate Object Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[7]  Kilian Q. Weinberger,et al.  Deep Networks with Stochastic Depth , 2016, ECCV.

[8]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[10]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.