Person Re-identification Using Two-Stage Convolutional Neural Network

Person re-identification is a fundamental task in automated video surveillance and has been an area of intensive research in the past few years. Several person re-identification methods based on deep learning have been proposed and achieved remarkable performance. However, extraction of more useful spatial and temporal information from input images and design of a more effective approach to match the same persons are still challenging. In this paper, we present a novel Two-Stage Convolution Neural Network (TSCNN), which effectively extracts the spatio-temporal feature with two-stream network in two directions, and matches the person with a novel convolutional neural network. Extensive experiments are conducted on three public benchmarks, i.e., iLIDS-VID, PRID2011 and MARS datasets. The experimental results demonstrate that the performance of our TSCNN is better in comparison with the state-of-the-art methods. The code of TSCNN is available at https://github.com/zyoohv/TSCNN.

[1]  Shengcai Liao,et al.  Deep Metric Learning for Person Re-identification , 2014, 2014 22nd International Conference on Pattern Recognition.

[2]  Cordelia Schmid,et al.  EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Shengcai Liao,et al.  Embedding Deep Metric for Person Re-identification: A Study Against Large Variations , 2016, ECCV.

[4]  Jin Wang,et al.  Temporally aligned pooling representation for video-based person re-identification , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[5]  Jesús Martínez del Rincón,et al.  Recurrent Convolutional Network for Video-Based Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Lin Wu,et al.  PersonNet: Person Re-identification with Deep Convolutional Neural Networks , 2016, ArXiv.

[7]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

[8]  Yang Li,et al.  Sparse re-id: Block sparsity for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  Bir Bhanu,et al.  Person Reidentification With Reference Descriptor , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Shaogang Gong,et al.  Person Re-Identification by Discriminative Selection in Video Ranking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Kuk-Jin Yoon,et al.  Improving Person Re-identification via Pose-Aware Multi-shot Matching , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Yang Li,et al.  Multi-Shot Human Re-Identification Using Adaptive Fisher Discriminant Analysis , 2015, BMVC.

[14]  Xiaojing Chen,et al.  Person Re-identification by Multi-hypergraph Fusion , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Xiang Li,et al.  Top-Push Video-Based Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Gang Wang,et al.  A Siamese Long Short-Term Memory Architecture for Human Re-identification , 2016, ECCV.

[18]  Bingpeng Ma,et al.  A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Anurag Mittal,et al.  Deep Neural Networks with Inexact Matching for Person Re-Identification , 2016, NIPS.

[20]  Bingbing Ni,et al.  Person Re-identification via Recurrent Feature Aggregation , 2016, ECCV.

[21]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Qi Tian,et al.  Person Re-identification in the Wild , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Zhen Zhou,et al.  See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Vittorio Murino,et al.  SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features , 2014, Person Re-Identification.

[26]  Deqiang Ouyang,et al.  Video-based person re-identification via spatio-temporal attentional and two-stream fusion convolutional networks , 2019, Pattern Recognit. Lett..

[27]  Yu Cheng,et al.  Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).