Incremental Kernel Null Foley-Sammon Transform for Person Re-identification

Person re-identification (Re-ID) is an important technique for video surveillance and security systems. Most existing Re-ID methods assume fixed size of training data and the models have to be re-trained from scratch given newly collected data, which is time-consuming. Accelerating the training speed with ever-increasing data is desired and critical for Re-ID. In this work, we propose to apply incremental learning to address this problem. We build the Re-ID model based on the null Foley-Sammon transform (NFST) method. Our idea is to extract new information from newly-added data and integrate it with the existing NFST trained model by an efficient updating scheme. We derived the incremental learning algorithm for both the non-kernelized and kernelized version of NFST. Extensive experiments have been carried on three public datasets, including VIPeR, PRID2011 and CUHK01. The results show that our proposed method can achieve comparable accuracy to the batch learning method while significantly reduces the computational complexity.