Light Person Re-Identification by Multi-Cue Tiny Net

Abstract

Nowadays, person re-identification (re-id) receives much attraction and it is for matching person images across disjoint camera views. Although many methods are developed, none of the state-of-the-art models (especially for the deep models) can be deployed by a camera's chip due to limited memory and weak computation. To address this problem, we propose a framework called Multi-Cue Tiny Net, which is a combination of tiny convolutional neural networks (CNNs) with small model size. And we call the person re-identification based on limited memory and weak computation the Light Person Re-Identification. Three different tiny nets are used to learn the complementary features and four cues of images are used for learning features with different information. All the features will be concatenated to form a fusion feature. Besides, to reduce the dimension of the features, and keep the small model size and high performance accuracy, we pre-trained the tiny nets and then retrained them after adding a fully connected layer to the global average pooling layer. Experimental results on challenging person re-identification dataset show that our approach yields promising accuracy with light memory and computation.