Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification

Wu, Lin; Wang, Yang; Yin, Hongzhi; Wang, Meng; Shao, Ling

doi:10.1109/tip.2019.2940684

Cited by 87 publications

(32 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 1 , the results obtained with the proposed method are compared with current key works of the state-of-the-art. In particular, the comparison was performed with deep network based methods, namely, RNN [ 42 ], CNN + XQDA + MQ [ 40 ], Spatial and Temporal RNN (SPRNN) [ 43 ], Attentive Spatial-Temporal Pooling Network (ASTPN) [ 44 ], Deep Siamese Network (DSAN) [ 50 ], PersonVLAD + XQDA [ 45 ], VRNN + KissME [ 47 ], and Superpixel-Based Temporally Aligned Representation (STAR) [ 49 ]. Regarding the proposed method, 5 different versions were used for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, the method attends human body part appearance and motion simultaneously and aggregates the extracted features via the vector of locally aggregated descriptors (VLAD) [ 46 ] aggregator. By considering the adversarial learning approach, in Reference [ 47 ] the authors presented a deep few-shot adversarial learning to produce effective video representations for video-based person re-identification, using few labelled training paired videos. In detail, the method is based on Variational Recurrent Neural Networks (VRNNs) [ 48 ], which can capture temporal dynamics by mapping video sequences into latent variables.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

View full text Add to dashboard Cite

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, Deep learning [86,31,74,87,88,77,75,76,60,22,57,85,84] technique as a kind of exceedingly powerful and efficient tool is applied for the tasks of person Re-Id. Combining hand-crafted histogram features and CNN features, Wu et al [89] presented a novel feature extraction model called Feature Fusion Net (FFN) to generate a novel deep feature representation which is more discriminative and compact.…”

Section: Person Re-id Via Supervised Learningmentioning

confidence: 99%

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

et al. 2020

View full text Add to dashboard Cite

Person re-identification (person Re-Id) aims to retrieve the pedestrian images of a same person that captured by disjoint and non-overlapping cameras. Lots of researchers recently focuse on this hot issue and propose deep learning based methods to enhance the recognition rate in a supervised or unsupervised manner. However, two limitations that cannot be ignored: firstly, compared with other image retrieval benchmarks, the size of existing person Re-Id datasets are far from meeting the requirement, which cannot provide sufficient pedestrian samples for the training of deep model; secondly, the samples in existing datasets do not have sufficient human motions or postures coverage to provide more priori knowledges for learning. In this paper, we introduce a novel unsupervised pose augmentation cross-view person Re-Id scheme called PAC-GAN to overcome these limitations. We firstly present the formal definition of cross-view pose augmentation and then propose the framework of PAC-GAN that is a novel conditional generative adversarial network (CGAN) based approach to improve the performance of unsupervised corss-view person Re-Id. Specifically, The pose generation model in PAC-GAN called CPG-Net is to generate enough quantity of pose-rich samples from original image and skeleton samples. The pose augmentation dataset is produced by combining the synthesized poserich samples with the original samples, which is fed into the corss-view person Re-Id model named Cross-GAN. Besides, we use weight-sharing strategy in the CPG-Net to improve the quality of new generated samples. To the best of our knowledge, we are the first try to enhance the unsupervised cross-view person Re-Id by pose augmentation, and the results of extensive experiments show that the proposed scheme can combat the state-of-the-arts.

show abstract

“…As for another person REID task of video based person REID [22], [23], Wu et al [24] presented the global deep video representation learning to view-based person re-identification that aggregates local 3D features across the entire video extent. The work [25] proposed a novel few-shot deep learning approahch to video-based person REID, to learn comparable representations that are discriminative and view-invariant. The research [26] designed a visual-appearance-level and spatial-temporal-level dictionary learning approach for video-based person REID task.…”

Section: Related Work a Image Based Person Re-identificationmentioning

confidence: 99%

Part-Based Enhanced Super Resolution Network for Low-Resolution Person Re-Identification

Tian

Miao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption when traditional low-resolution person REID models are under pixel-to-pixel supervision in low and high resolution pedestrian image pairs. In addition, they are easily influenced by the global background, illumination or pose variations across camera views. Therefore, we propose a Part-based Enhanced Super Resolution (PESR) network by employing a part division strategy and an enhanced generative adversarial network to boost the unpaired pedestrian image super resolution process. Specifically, the part-based super resolution network transforms low resolution image in probe into high resolution without any pixel-to-pixel supervision and the part-based synthetic feature extractor module can learn discriminative pedestrian feature representation for the generated high resolution images, which employ a part feature connection loss as constraint to conduct matching for person re-identification. Furthermore, evaluations on four public person REID datasets demonstrate the advantages of our method over the state-of-the-art ones. INDEX TERMS Low-resolution person re-identification, enhanced super resolution, part based, realistic discriminator.

show abstract

Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification

Cited by 87 publications

References 101 publications

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

Part-Based Enhanced Super Resolution Network for Low-Resolution Person Re-Identification

Contact Info

Product

Resources

About