Abstract
Pedestrian reidentification has recently emerged as a hot topic that attains considerable attention since it can be applied to many potential applications in the surveillance system. However, high-accuracy pedestrian reidentification is a stimulating research problem because of variations in viewpoints, color, light, and other reasons. This work addresses the interferences and improves pedestrian reidentification accuracy by proposing two novel algorithms, pedestrian multilabel learning, and investigating hybrid learning metrics. First, unlike the existing models, we construct the identification framework using two subnetworks, namely, part detection subnetwork and feature extraction subnetwork, to obtain pedestrian attributes and low-level feature scores, respectively. Then, a hybrid learning metric that combines pedestrian attributes and low-level feature scores is proposed. Both low-level features and pedestrian attributes are utilized, thus enhancing the identification rate. Our simulation results on both datasets, i.e., CUHK03 and VIPeR, reveal that the identification rate is improved compared to the existing pedestrian reidentification methods.
Highlights
Pedestrian reidentification emerges as a hot research topic. It has attained considerable attention since it can be applied to many potential applications in humancomputer interaction and surveillance tasks. e purpose of pedestrian reidentification algorithms is to search and detect a target from a large set of images
In recent years, pedestrian reidentification emerges as a hot research topic
For supervised learning-based approaches [3, 4], the work presented in [1] used a deep convolutional neural network (CNN) for learning features continuously, and the resultant matching metrics used in reidentification of individuals
Summary
Pedestrian reidentification emerges as a hot research topic. It has attained considerable attention since it can be applied to many potential applications in humancomputer interaction and surveillance tasks. e purpose of pedestrian reidentification algorithms is to search and detect a target from a large set of images. E purpose of pedestrian reidentification algorithms is to search and detect a target from a large set of images. For supervised learning-based approaches [3, 4], the work presented in [1] used a deep convolutional neural network (CNN) for learning features continuously, and the resultant matching metrics used in reidentification of individuals Extracting, embedding, and evaluating features from different image domains are critical in developing a high-performance pedestrian reidentification framework.
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