Deep learning-based methods for person re-identification: A comprehensive review

Wu, Di; Zheng, Shouzhu; Zhang, Xiaoping; Yuan, Changan; Cheng, Fei; Zhao, Yang; Lin, Yong-Jun; Zhao, Zhong-Qiu; Jiang, Yong-Li; Huang, De-Shuang

doi:10.1016/j.neucom.2019.01.079

Cited by 186 publications

(75 citation statements)

References 37 publications

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“…The vast majority of the literature on person ReID makes use of RGB images, as detailed in the review from Bedagkar-Gala et al [21] and the more recent deep-learning review from Wu et al [22]. Deep-learning algorithms on RGB images constitute the state-of-the-art in person ReID; however, as addressed in [22], the majority of them are mainly focused on short-term scenarios. Person ReID for home monitoring includes very demanding challenges, such as strong change in appearance (i.e., clothes), appearance impaired scenarios, etc., that need to be specifically addressed.…”

Section: Reid From Imagesmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.

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Section: Reid From Imagesmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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“…In this section, we review the research works on unsupervised person re-identification, which is divided as clustering based person re-id and cross domain person re-id methods according to surveys [9], [24]. In addition, this paper introduce a multi-task learning architecture into CDPR model, so we also discuss some multi-task learning approaches in supervised person re-identification.…”

Section: Related Workmentioning

confidence: 99%

Deep Multi-Task Transfer Network for Cross Domain Person Re-Identification

Wang

2020

IEEE Access

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As a prominent application of surveillance video analysis, person re-identification attracts much more research attention recently. Existing person re-identification models often focus on supervision by the pedestrian identity annotation, while it has limited scalability in realistic. Though several unsupervised person re-identification researches pay attention to solve this problem, they are either clustering based or cross domain based approaches, where a conventional assumption of them is the identity number of the target dataset is acknowledged. To relax this hypothesis, we propose a Deep Multi-task Transfer Network (DMTNet) for cross domain person re-identification, which conduct classification, attribute attention and identification task between source and target domains. There are three main novelties in DMTNet, including clustering number estimating algorithm to learn prior knowledge from source data to estimate the identity number, attribute attention importance learning rather than directly utilizing attribute information, and a multi-task transfer learning mechanism to transfer specific tasks cross domains. To prove the superiority of our DMTNet, we implement several compared experiments on DukeMTMC-reID and Market-1501 datasets, which results show the advancement of our network. Moreover, the discussions for different modules also point out the significance of the specific tasks. INDEX TERMS Cross domain person re-identification, multi-task transfer, attribute attention, identity number estimating.

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“…In a recent survey, existing deep learning-based person re-identification methods achieve encouraging results on large-scale person re-identification datasets but degrade when applied in real-world environments that require robustness against many cameras and variations across a long period [9]. The training-at-once approach relies on short-term-collected datasets, which over time would struggle to achieve and maintain sufficient generalization for dealing with rapidly growing new data.…”

Section: Related Workmentioning

confidence: 99%

“…Measuring similarity: verification-based person reidentification model [9] is applied to calculate similarities between input images. Initially, a query image is fed into the network to extract the features.…”

Section: Resnet Base Modelmentioning

confidence: 99%

Continuous Learning without Forgetting for Person Re-Identification

Sugianto

Tjondronegoro

Sorwar

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Deep learning-based person re-identification faces a scalability challenge when the target domain requires continuous learning. Service environments, such as airports, need to recognize new visitors and add new cameras over time. Training-at-once is not enough to make the model robust to new tasks and domain variations. A well-known approach is fine-tuning, which suffers forgetting problem on old tasks when learning new tasks. Joint-training can alleviate the problem but requires old datasets, which is unobtainable in some cases. Recently, Learning without forgetting (LwF) shows its ability to mitigate the problem without old datasets. This paper extends the benefit of LwF from image classification to person re-identification with further challenges. Comprehensive experiments are based on Market1501 and DukeMTMC4ReID to evaluate and benchmark LwF to other approaches. The results confirm that LwF outperforms fine-tuning in preserving old knowledge and joint-training in faster training.

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Deep learning-based methods for person re-identification: A comprehensive review

Cited by 186 publications

References 37 publications

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Deep Multi-Task Transfer Network for Cross Domain Person Re-Identification

Continuous Learning without Forgetting for Person Re-Identification

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