IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):4894-4912. doi: 10.1109/TPAMI.2021.3079910. Epub 2022 Aug 4.
Person re-identification (reID) plays an important role in computer vision. However, existing methods suffer from performance degradation in occluded scenes. In this work, we propose an occlusion-robust block, Region Feature Completion (RFC), for occluded reID. Different from most previous works that discard the occluded regions, RFC block can recover the semantics of occluded regions in feature space. First, a Spatial RFC (SRFC) module is developed. SRFC exploits the long-range spatial contexts from non-occluded regions to predict the features of occluded regions. The unit-wise prediction task leads to an encoder/decoder architecture, where the region-encoder models the correlation between non-occluded and occluded region, and the region-decoder utilizes the spatial correlation to recover occluded region features. Second, we introduce Temporal RFC (TRFC) module which captures the long-term temporal contexts to refine the prediction of SRFC. RFC block is lightweight, end-to-end trainable and can be easily plugged into existing CNNs to form RFCnet. Extensive experiments are conducted on occluded and commonly holistic reID benchmarks. Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets. The source code is available at https://github.com/blue-blue272/OccludedReID-RFCnet.
人体重识别（reID）在计算机视觉中起着重要作用。然而，现有的方法在遮挡场景中性能下降。在这项工作中，我们提出了一种遮挡鲁棒块，区域特征补全（RFC），用于遮挡 reID。与大多数以前的工作不同，RFC 块可以在特征空间中恢复遮挡区域的语义，而不是丢弃遮挡区域。首先，开发了一种空间 RFC（SRFC）模块。SRFC 利用非遮挡区域的长程空间上下文来预测遮挡区域的特征。单元级预测任务导致了一个编码器/解码器结构，其中区域编码器模型化了非遮挡区域和遮挡区域之间的相关性，而区域解码器利用空间相关性来恢复遮挡区域特征。其次，我们引入了时间 RFC（TRFC）模块，它捕获长程时间上下文，以细化 SRFC 的预测。RFC 块是轻量级的，端到端可训练的，可以很容易地插入到现有的 CNN 中，形成 RFCnet。在遮挡和常见的整体 reID 基准上进行了广泛的实验。我们的方法在遮挡数据集上明显优于现有的方法，而在整体数据集上仍然保持着卓越的性能。源代码可在 https://github.com/blue-blue272/OccludedReID-RFCnet 获得。
