Person Re-Identification (ReID) matches pedestrians across disjoint cameras. Existing ReID methods adopting real-value feature descriptors have achieved high accuracy, but they are low in efficiency due to the slow Euclidean distance computation as well as complex quick-sort algorithms. Recently, some works propose to yield binary encoded person descriptors which instead only require fast Hamming distance computation and simple counting-sort algorithms. However, the performances of such binary encoded descriptors, especially with short code (e.g., 32 and 64 bits), are hardly satisfactory given the sparse binary space. To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by $0.25$ times than real-value features under the same dimensions whilst maintaining a competitive accuracy, especially under short codes. SCR transforms real-value features vector (e.g., 2048 float32) with short binary codes (e.g., 64 bits) by first dividing real-value features vector into $M$ sub-spaces, each with $C$ clustered centroids. Thus the distance between two samples can be expressed as the summation of the respective distance to the centroids, which can be sped up by offline calculation and maintained via a look-up table. On the other side, these real-value centroids help to achieve significantly higher accuracy than using binary code. Lastly, we convert the distance look-up table to be integer and apply the counting-sort algorithm to speed up the ranking stage. We also propose a novel consistency regularization with an iterative framework. Experimental results on Market-1501 and DukeMTMC-reID show promising and exciting results. Under short code, our proposed SCR enjoys Real-value-level accuracy and Hashing-level speed.

翻译：个人重新识别( ReID) 匹配不连接相机行人。 使用真实价值特征描述仪的现有 ReID 方法已经取得了很高的准确性, 但效率较低, 原因是 Euclidean 远程计算缓慢, 以及复杂的快速 Sort 算法。 最近, 一些作品建议生成二进制的编码个人描述仪, 而这只要求快速模拟距离计算和简单计数算算算算法。 然而, 使用实际价值描述仪的二进制描述仪的性能, 特别是短代码( 例如, 32 和 64 比 位) 。 由于二进制空空空空间空间描述仪空间描述仪空间描述仪的准确性能比较缓慢。 为了在模型的准确性能之间取得平衡, 新的次空格描述器描述器的精确性能比实际价值描述值增加0.25倍, 而在短代码下, SC 将实际价值描述的精度描述仪表直径值转换为直径向下( 例如, 64 比 ) 直径, 通过第一次进行真实的直径表示的直径表示的直径方表示的直径方计算, 直径直方计算值, 。