Target re-identification based on adaptive incremental KISS measure learning

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• 作者：Wei Cao ; Hua Han ; Xian-kun Sun ; Zhi-jun Fang
• 关键词：KISSME ; Eigenvalue stabilization technique ; AIKSSME ; Virtual sample
• 刊名：Memetic Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：9
• 期：1
• 页码：23-30
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Complex Systems; Control, Robotics, Mechatronics; Bioinformatics; Applications of Mathematics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1865-9292
• 卷排序：9

文摘

Target re-identification from across cameras is a difficult problem in multi-camera surveillance, which needs to be urgently solved. Traditional solutions, in addition to relying on the statistical characteristics of targets’ appearance, are more often using excellent measurement algorithms. Among many such algorithms, the Keep It Simple and Stupid Measure Learning (KISSME) algorithm based on statistical probability is an outstanding one. But it has a problem that the eigenvalue is not stable, and the actual matching rate is relatively low. So, in this paper, we optimize the measurement algorithms based on large scale Keep It Simple and Stupid (KISS) measure learning. From elements, such as inadequate sample, size and smaller or larger eigenvalues, we introduce eigenvalue stabilization technique, and finally form our algorithm which can be called Adaptive Incremental Keep It Simple and Stupid Measure Learning (AIKISSME). Finally, through many experiments based on Viewpoint Invariant Pedestrian Recognition (VIPeR) and by comparing with other algorithms, this work concludes that AIKISSME achieves the best overall performance.