Roberto Vezzani
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Abstract
The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.
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Index Terms
- People reidentification in surveillance and forensics: A survey
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Reviews
Anca Doloc-Mihu
This survey paper presents the challenges and existing solutions for people reidentification, which aims at assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. The authors propose a multidimensional (instead of hierarchical) categorization of the existing approaches, which includes the following tasks per each dimension: camera setting, sample set, signature, body model, machine learning algorithms, and application scenario. Camera settings include descriptions of the type of recorded visual data and the global layout of the camera used. Sample set describes one or multiple shots from one person. The signature specifies the set of features collected from the samples and used to provide a discriminative profile for each person, and could be a single feature or a combination of features that include color, shape, position, texture, and soft biometry. Machine learning algorithms can be used to refine the descriptions of a person. The paper presents the very few (ten) available datasets used to test the reidentification approaches, and the metrics used for evaluating the performance of their algorithms. It overviews approaches from more than 100 other papers on the subject. Online Computing Reviews Service
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