Gait recognition for person re-identification
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Gait recognition for person re‑identification Omar Elharrouss1 · Noor Almaadeed1 · Somaya Al‑Maadeed1 · Ahmed Bouridane2
© The Author(s) 2020
Abstract Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset. Keywords Gait recognition · Angle estimation · Motion detection · Convolutional neural networks
* Omar Elharrouss [email protected] Extended author information available on the last page of the article
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1 Introduction Across multiple cameras, person recognition and identification are important targets for many computer vision applications, especially monitoring systems [1]. The operation of recognition of a person from a set of images captured by several cameras is called person re-identification. The similarity measures can be the key to compute the matching between two or a set of images. However, the re-identification using video clips can be a problem for many applications [2], for example people tracking across multiple cameras. The video sequences captured by different cameras should be analyzed to re-identify the person and keep tracking him across all cameras in the surveilled areas. The sequential methods that use a list of features are not efficient for the reidentification of persons due to several limitations such as differences between the analyzed objects in terms of shape, colors, scales, and others [3]. which, in turn, implies that the use of a limited number of features cannot be enough for proper identification. On the other hand, with deep learning techniques, the use of different and non-limited f
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