Adversarial Binary Coding for Efficient Person Re-Identification

Abstract

Person re-identification (ReID) aims at matching persons across different views/scenes. In addition to accuracy, the matching efficiency has received more and more attention because of demanding applications using large-scale data. Several binary coding based methods have been proposed for efficient ReID, which either learn projections to map high-dimensional features to compact binary codes, or directly adopt deep neural networks by simply inserting an additional fully-connected layer with tanh-like activations. However, the former approach requires time-consuming hand-crafted feature extraction and complicated (discrete) optimizations; the latter lacks the necessary discriminative information greatly due to the straightforward activation functions. In this paper, we propose a simple yet effective framework for efficient ReID inspired by the recent advances in adversarial learning. Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively. The discriminability of the extracted codes is further enhanced by equipping the ABC with a deep triplet network for the ReID task. More importantly, the ABC and triplet network are simultaneously optimized in an end-to-end manner. Extensive experiments on three large-scale ReID benchmarks demonstrate the superiority of our approach over the state-of-the-art methods.

An Efficient Person Re-Identification Model Based on New Regularization Technique

“…These differences are mainly determined by training losses/objectives. The authors in [49] presented a framework depending on adversarial binary coding (ABC) for efficient person re-identification, which efficiently produces binary and discriminative features from pedestrian images. After that, a trained discriminator network was employed for differentiating between the real-valued features and the binary ones, so that it can lead the feature extractor network to produce features in binary form under the Wasserstein loss.…”

“…Our binary coding method depends on the method presented by Liu et al . [49], who proposed a method called adversarial binary coding inspired by GANs. Our method is different from their method since we utilized our proposed regularization in the discriminative model.…”

“…Moreover, unstable optimization may occur because of the absence of normalization. Therefore, we followed the method of [49] to normalize the output feature vectors and sampled binary codes.…”

“…It includes both manually and automatically labeled bounding boxes with different sizes. All the images are resized to be 160 60 as conducted by [49], and 20% train/test splits criteria are followed as done by [13]. The number of iterations was set to 6,000, and α values were set to 0.2 and increased to 0.5 with an increment of 0.1 after every 1,000 iterations.…”

“…Therefore, this is the main method to which we compare our results. 3) 512-bit ABC+triplet [49] Results concerning CUHK03 and Market-1501 datasets are displayed in Tables 1 and 2, respectively. Figures 3 to 7 present the triplet and discriminator loss on the three datasets.…”