Double-Line Multi-scale Fusion Pedestrian Saliency Detection

Pedestrian salient detection aims at identifying person body parts in occluded person images, which is greatly significant in occluded person re-identification. To achieve pedestrian salient detection, we propose a double-line multi-scale fusion (DMF) network, which not only extracts double-line features and retains both high-level and low-level semantic information but also fuses high-level information and low-level information for better complement. CRF is then used to further improve its performance. Finally, our method is used to deal with occluded person images into partial person images to achieve partial person re-identification matching. Experiment results on five benchmark datasets show the superiority of our proposed method, and result on two occluded person re-identification datasets indicate the effectiveness of our proposal on pedestrian salient detection.

[1]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[2]  Yizhou Yu,et al.  Visual saliency based on multiscale deep features , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yizhou Yu,et al.  Deep Contrast Learning for Salient Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Huchuan Lu,et al.  Deep networks for saliency detection via local estimation and global search , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Hong Liu,et al.  Saliency detection via global-object-seed-guided cellular automata , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[9]  Jianhuang Lai,et al.  P2SNet: Can an Image Match a Video for Person Re-Identification in an End-to-End Way? , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Xiaogang Wang,et al.  Saliency detection by multi-context deep learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Xiang Li,et al.  Partial Person Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jian-Huang Lai,et al.  Person Re-Identification by Camera Correlation Aware Feature Augmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Jian-Huang Lai,et al.  Person re-identification with multi-level adaptive correspondence models , 2015, Neurocomputing.

[17]  Zhiming Luo,et al.  Non-local Deep Features for Salient Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Jian-Huang Lai,et al.  Multi-shot Person Re-identification with Automatic Ambiguity Inference and Removal , 2014, 2014 22nd International Conference on Pattern Recognition.

[19]  Jian Sun,et al.  Saliency Optimization from Robust Background Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Jian-Huang Lai,et al.  Occluded Person Re-Identification , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[21]  Jian Sun,et al.  Geodesic Saliency Using Background Priors , 2012, ECCV.

[22]  Jian-Huang Lai,et al.  Deep Ranking for Person Re-Identification via Joint Representation Learning , 2015, IEEE Transactions on Image Processing.

[23]  James M. Rehg,et al.  The Secrets of Salient Object Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.