Peking University

Shenzhen Chuangwei-rgb Electronics Co., Ltd.

Yihao Zhang

Wen Gao

Qiang Li

Ge Li

Ronggang Wang

Zhenyu Wang

Ying Li

Wenmin Wang

Shengfu Dong

Xiangcheng Liu

Hui Zhao

Shuxiao Wei

Bosheng Tang

Linrong Feng

Xingkang Liu

Television

Speaker

An image feature extraction method for person re-identification includes performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction; performing the aligned local descriptor extraction by processing an original image by affine transformation and performing a summation pooling operation on image block features of same regions to obtain an aligned local descriptor; reserving spatial information between inner blocks of the image for the aligned local descriptor; and performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature. The method can resolve the problem of feature misalignment caused by posture changes of pedestrian, etc., and eliminate the effect of unrelated backgrounds on re-recognition, thus improving the precision and robustness of person re-identification.

Image feature extraction method for person re-identification

Disclosed is a deep discriminative network for person re-identification in an image or a video. Concatenation are carried out on different input images on a color channel by constructing a deep discriminative network, and an obtained splicing result is defined as an original difference space of different images. The original difference space is sent into a convolutional network. The network outputs the similarity between two input images by learning difference information in the original difference space, thereby realizing person re-identification. The features of an individual image are not learnt, and concatenation are carried out on input images on a color channel at the beginning, and difference information is learnt on an original space of the images by using a designed network. By introducing an Inception module and embedding the same into a model, the learning ability of a network can be improved, and a better differentiation effect can be achieved.

Method of using deep discriminate network model for person re-identification in image or video

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Shenzhen, China

Yihao Zhang