A Novel Incremental Person Re-recognition Method with Constant Update Speed

摘要

Person re-identification (Re-ID) is an important technique towards the automatic search of a person's presence in a surveillance video or security systems. Applying incremental learning techniques to accelerate the online training speed with ever-increasing data is desired and critical for Re-ID. As an incremental learning algorithm, Incremental Kernel Null Foley-Sammon Transform (IKNFST) method significantly reduces the computational complexity while holds the accuracy. However, with ever-increasing person samples within the same category, the corresponding growth of dimensions makes it difficult to update the online model. To address the issue, we propose to maintain constant update speed by constructing Reduce Set (RS) expansions during online updating. The key idea is to firstly extract new information brought by newly-added samples and integrate it with the existing model by Incremental Kernel Principal Component Analysis (IKPCA) scheme for further Reduce Set (RS) compression. And the compressed samples and the corresponding model are then input to Kernel Null Foley-Sammon Transform (KNFST) algorithm for generating an updated model. Extensive experiments have been carried on three public datasets, including Market-1501, DukeMTMC-RelD and CUHK03. The results show that our proposed method beats the state-of-the-art IKNFST by a big margin.