Abstract
Recently, the applications of person re-identification in visual surveillance and human-computer interaction are sharply increasing, which signifies the critical role of such a problem. In this paper, we propose a two-stream convolutional network (ConvNet) based on the competitive similarity aggregation scheme and co-attentive embedding strategy for video-based person re-identification. By dividing the long video sequence into multiple short video snippets, we manage to utilize every snippet's RGB frames, optical flow maps and pose maps to facilitate residual networks, e.g., ResNet, for feature extraction in the two-stream ConvNet. The extracted features are embedded by the co-attentive embedding method, which allows for the reduction of the effects of noisy frames. Finally, we fuse the outputs of both streams as the embedding of a snippet, and apply competitive snippet-similarity aggregation to measure the similarity between two sequences. Our experiments show that the proposed method significantly outperforms current state-of-the-art approaches on multiple datasets.
Abstract (translated)
近年来，人的再识别在视觉监控和人机交互中的应用日益广泛，这标志着人的再识别问题的关键作用。本文提出了一种基于竞争相似性聚合方案的双流卷积网络（convnet），并提出了一种基于视频的人再识别协同注意嵌入策略。通过将长视频序列划分为多个短视频片段，我们设法利用每个片段的RGB帧、光流图和姿势图，以便于残留网络（例如resnet）在双流convnet中进行特征提取。提取的特征采用共聚焦嵌入方法进行嵌入，减少了噪声帧的影响。最后，我们将两个流的输出融合为一个片段的嵌入，并应用竞争片段相似性聚合来度量两个序列之间的相似性。我们的实验表明，该方法在多个数据集上显著优于当前最先进的方法。
URL
https://arxiv.org/abs/1905.11862
