Automated person re-identification in a multi-camera surveillance setup is very important for effective tracking and monitoring crowd movement. In this paper, we propose an efficient hierarchical re-identification approach in which color histogram-based comparison is employed to find the closest matches in the gallery set, and next deep feature-based comparison is carried out using the Siamese network. Reduction in search space after the first level of matching helps in improving the accuracy as well as efficiency of prediction by the Siamese network by eliminating dissimilar elements. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body parts and make the appearance descriptors more discriminating. The proposed approach has been evaluated on five public data sets and also a new data set captured in our laboratory. Results reveal that it outperforms most state-of-the-art approaches in terms of overall accuracy.

在多摄像机监视设置中自动进行人员重新识别对于有效跟踪和监视人群移动非常重要。在本文中，我们提出了一种有效的分层重识别方法，其中使用基于颜色直方图的比较来找到画廊集中最接近的匹配项，然后使用暹罗网络进行基于深度特征的下一个比较。在第一级匹配之后减少搜索空间有助于通过消除相异元素来提高暹罗网络的预测准确性和效率。在每个层次的层次结构中采用基于轮廓部分的特征提取方案，以保留不同身体部位的相对位置，并使外观描述符更具区分性。该提议的方法已经在五个公共数据集以及我们实验室中捕获的新数据集上进行了评估。结果表明，就整体准确性而言，它优于大多数最新方法。