Abstract
Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks.
Highlights
Pedestrians are reidentified in two steps: manually extracting features from pedestrian images, e.g., using a color histogram and a histogram of oriented gradients (HOG) [1], and learning the measurement matrix via similarity measurement methods such as the large margin nearest-neighbor (LMNN) [2] and cross-view quadratic discriminant analysis (XQDA) [3]
The pedestrian features are extracted from the original image with a convolutional neural network (CNN) and judged to determine whether they belong to the same pedestrian
The average test time of pedestrian multiview GAN (PmGAN)+PCB, PmGAN+Multiple Granularity Network (MGN) and PmGAN+Pyramidal was very close to the test time of PCB, MGN and Pyramida (The gap is less than 1%)
Summary
Pedestrians are reidentified in two steps: manually extracting features from pedestrian images, e.g., using a color histogram and a histogram of oriented gradients (HOG) [1], and learning the measurement matrix via similarity measurement methods such as the large margin nearest-neighbor (LMNN) [2] and cross-view quadratic discriminant analysis (XQDA) [3]. By treating PRI as a classification task, these methods learn pedestrian features through network training. Through visual analysis of the images reidentified by these types of methods, it is learned that the distance between an image and the target image, and the identification probability of that image, is negatively correlated with their similarity of view. This obviously limits the performance of DL-based PRI methods using metric learning. This paper proposes a novel PRI method based on the pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The view label was introduced such that the discriminator could discriminate multiview images. (3) The proposed PRI method was verified through experiments on three mainstream datasets
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