A Two Stream Siamese Convolutional Neural Network for Person Re identification
Person re identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this paper, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also propose a weighted two stream training objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person's identity. We evaluate our proposed method on the publicly available PRID2011 and iLIDS VID datasets and demonstrate the efficacy of our proposed method. On average, the top rank matching accuracy is 4% higher than the accuracy achieved by the cross view quadratic discriminant analysis used in combination with the hierarchical Gaussian descriptor (GOG+XQDA) and 5% higher than the recurrent neural network method.
2017 IEEE International Conference on Computer Vision (ICCV)