A coarse to fine deep learning for person re identification
This paper proposes a novel deep learning architecture for person re identification. The proposed network is based on a coarse to fine learning (CFL) approach, attempting to acquire a generic to specific knowledge throughout a transfer learning process. The core of the method relies on a hybrid network composed of a convolutional neural network and a deep belief network denoising autoenconder. This hybrid network is in charge of extracting features invariant to illumination varying, certain image deformations, horizontal mirroring and image blurring, and is embedded in the CFL architecture. The proposed network achieved the best results when compared with other state of the arts methods on i LIDS, CUHK01 and CUHK03 data sets, and also a competitive performance on VIPeR data set.
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)