一种基于时空特征的遮挡像素重建新型端到端框架及其在行人重识别中的改进应用
A Novel end-to-end Framework for Occluded Pixel Reconstruction with Spatio-temporal Features for Improved Person Re-identification
AI总结：
本文提出一种端到端框架，结合CNN遮挡检测、Conv-LSTM/Autoencoder像素重建和cGAN细化，显著提升了遮挡场景下行人重识别的Rank-1精度。
摘要：
行人重识别对于监测和追踪人群流动以增强公共安全至关重要。然而，在存在遮挡的情况下进行重识别会显著降低现有系统的性能，这是一个具有挑战性的领域。在本工作中，我们通过开发一种由深度神经网络组成的、面向RGB图像/视频的有效遮挡检测与重建框架，为该问题提出了一种可行的解决方案。具体而言，一个基于CNN的遮挡检测模型对各个输入帧进行分类，随后分别使用Conv-LSTM和Autoencoder对顺序（视频）数据和非顺序（图像）数据中与遮挡帧对应的遮挡像素进行重建。重建后的RGB帧质量进一步通过条件生成对抗网络（cGAN）进行细化和微调。我们的方法在该领域四个知名的公共数据集上进行了评估，定性重建结果确实令人满意。以Siamese网络的重识别精度为指标的定量评估表明，在各种数据集上经过遮挡像素重建后取得了卓越的Rank-1精度。与最先进方法的对比分析也证明了我们的工作在现实监控系统中应用的鲁棒性。
英文摘要：
Person re-identification is vital for monitoring and tracking crowd movement to enhance public security. However, re-identification in the presence of occlusion substantially reduces the performance of existing systems and is a challenging area. In this work, we propose a plausible solution to this problem by developing effective occlusion detection and reconstruction framework for RGB images/videos consisting of Deep Neural Networks. Specifically, a CNN-based occlusion detection model classifies individual input frames, followed by a Conv-LSTM and Autoencoder to reconstruct the occluded pixels corresponding to the occluded frames for sequential (video) and non-sequential (image) data, respectively. The quality of the reconstructed RGB frames is further refined and fine-tuned using a Conditional Generative Adversarial Network (cGAN). Our method is evaluated on four well-known public data sets of the domain, and the qualitative reconstruction results are indeed appealing. Quantitative evaluation in terms of re-identification accuracy of the Siamese network showed an exceptional Rank-1 accuracy after occluded pixel reconstruction on various datasets. A comparative analysis with state-of-the-art approaches also demonstrates the robustness of our work for use in real-life surveillance systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
