Continuous Learning without Forgetting for Person Re-Identification

Sugianto, Nehemia; Tjondronegoro, Dian; Sorwar, Golam; Chakraborty, Prithwi Raj; Yuwono, Elizabeth Irenne

doi:10.1109/avss.2019.8909828

Cited by 14 publications

(3 citation statements)

References 22 publications

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“…This requires a Re-ID model to be incrementally generalised without forgetting knowledge already learned. In (Sugianto et al 2019), Sugianto et al apply the learning without forgetting method (LwF) (Li and Hoiem 2018) for continuous learning in Re-ID, but their method is a straightforward application of LwF, failing to address the inherent challenge of domain incremental generalisation in Re-ID. In this work, we characterise the lifelong Re-ID problem by unseen class recognition, domain generalisation and class imbalanced learning.…”

Section: Related Workmentioning

confidence: 99%

Generalising without Forgetting for Lifelong Person Re-Identification

Gong

2021

AAAI

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Existing person re-identification (Re-ID) methods mostly prepare all training data in advance, while real-world Re-ID data are inherently captured over time or from different locations, which requires a model to be incrementally generalised from sequential learning of piecemeal new data without forgetting what is already learned. In this work, we call this lifelong person Re-ID, characterised by solving a problem of unseen class identification subject to continuous new domain generalisation and adaptation with class imbalanced learning. We formulate a new Generalising without Forgetting method (GwFReID) for lifelong Re-ID and design a comprehensive learning objective that accounts for classification coherence, distribution coherence and representation coherence in a unified framework. This design helps to simultaneously learn new information, distil old knowledge and solve class imbalance, which enables GwFReID to incrementally improve model generalisation without catastrophic forgetting of what is already learned. Extensive experiments on eight Re-ID benchmarks, CIFAR-100 and ImageNet show the superiority of GwFReID over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Generalising without Forgetting for Lifelong Person Re-Identification

Gong

2021

AAAI

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“…To reduce the cost of labeling data and improve the scalability of the model, researchers have made a lot of attempts. Semi-supervised learning [143], [144], [146], weakly supervised learning [145], meta learning [262], small sample learning [147], [148], active learning [33], [149], [150], online learning [151], knowledge transfer [152], reinforcement learning [309]- [311], and learning without forgetting [260], [261] have also attracted the attention of person re-identification researchers. These studies have greatly promoted the exploration of open-world person re-identification.…”

Section: B: Other Means Of Learningmentioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference. INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.

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“…Serious data threats such as data theft, eavesdropping or cyber-attacks, can potentially occur during data transmission. Likewise, when AI-enabled video surveillance systems incorporate AI continual learning to adapt the AI models toward constant visual change in the environment, existing approaches require new data to be collected from cameras and retained long term (Sugianto et al , 2019). This also involves data privacy concerns due to potential data misuse.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

Sugianto

Tjondronegoro

Stockdale

et al. 2021

ITP

Self Cite

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PurposeThe paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.Design/methodology/approachThe paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.FindingsThe proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.Originality/valueThe paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

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Continuous Learning without Forgetting for Person Re-Identification

Cited by 14 publications

References 22 publications

Generalising without Forgetting for Lifelong Person Re-Identification

Generalising without Forgetting for Lifelong Person Re-Identification

A Comprehensive Overview of Person Re-Identification Approaches

Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

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