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.
Highlights
Across multiple cameras, person recognition and identification are important targets for many computer vision applications, especially monitoring systems [1]
The proposed method contains two phases: first the detection of moving objects based on background subtraction and the gait recognition which is performed by estimating the angle of view and recognizing the gait
Our approach involves the recognition of gait images based on viewing angle estimation of the gait energy images (GEIs) of input probe images
Summary
Person recognition and identification are important targets for many computer vision applications, especially monitoring systems [1]. In order to consolidate the visualized results, we use different metrics, including gray-level error (AGE), total number of error pixels (EPs), percentage of error pixels (pEPs), total number of clustered error pixels (CEPs), peak signalto-noise ratio (PSNR), multiscale structural similarity index (MS-SSIM), color image quality measure (CQM). The proposed method succeeds to modelize the background with good results in comparison with other methods in the most dataset videos including HighwayI, Hall & Monitor, Snellen, and Foliage
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