Open set person re-identification framework on closed set re-id systems

Vidanapathirana, Madhawa; Sudasingha, Imesha; Kanchana, Pasindu; Vidanapathirana, Jayan; Perera, Indika

doi:10.1109/siprocess.2017.8124507

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Vidanapathirana et al [13] proposed a method to use already developed descriptors in the multi-shot open-set case. They perform novelty detection by keeping a relatively large set of random individuals.…”

Section: Related Workmentioning

confidence: 99%

Open-Set Person Re-Identification Through Error Resilient Recurring Gallery Building

Witzig

Upenik

Ebrahimi

2021

2021 IEEE International Conference on Image Processing (ICIP)

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In person re-identification, individuals must be correctly identified in images that come from different cameras or are captured at different points in time. In the open-set case, the above needs be achieved for people who have not been previously recognised. In this paper, we propose a universal method for building a multi-shot gallery of observed reference identities recurrently online. We perform L2-norm descriptor matching for gallery retrieval using descriptors produced by a generic closed-set re-identification system. Multishot gallery is continuously updated by replacing outliers with newly matched descriptors. Outliers are detected using the Isolation Forest algorithm, thus ensuring that the gallery is resilient to erroneous assignments, leading to improved reidentification results in the open-set case.

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Section: Related Workmentioning

confidence: 99%

Open-Set Person Re-Identification Through Error Resilient Recurring Gallery Building

Witzig

Upenik

Ebrahimi

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Feature Extraction Methods in Person Re-identification System: A Technical Review

Jayavarthini

Malathy

2020

Lecture Notes in Electrical Engineering

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Identifying Re-identification Challenges: Past, Current and Future Trends

Qian,

Barthelemy,

Karuppiah

et al. 2024

SN COMPUT. SCI.

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Person and vehicle re-identification has been a popular subject in the field of the computer vision technologies. Existing closed-set re-identification surpasses human-level accuracies on commonly used benchmarks, and the research focus for re-identification is shifting to the open world-setting. The latter setting is more suitable for practical applications, however, is less developed due to its challenges. On the other hand, existing research is more focused on person re-identification, even though both, person and vehicle, are important components for smart city applications. This review attempts to combine for the first time the problem of person and vehicle re-identification under closed and open settings, its challenges, and the existing research. Specifically, we start from the origin of the re-identification task and then summarize state-of-the-art research based on deep learning in different scenarios: person or vehicle or unified re-identification in closed- and open-world settings. Additionally, we analyse a new method for solving the re-identification task using the Transformer, a model architecture that relies entirely on an attention mechanism, which shows promising results. This survey facilitates future research by providing a summary on past and present trends, and aids to improve the usability of re-ID techniques.

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Open set person re-identification framework on closed set re-id systems

Cited by 3 publications

References 13 publications

Open-Set Person Re-Identification Through Error Resilient Recurring Gallery Building

Open-Set Person Re-Identification Through Error Resilient Recurring Gallery Building

Feature Extraction Methods in Person Re-identification System: A Technical Review

Identifying Re-identification Challenges: Past, Current and Future Trends

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