A Stochastic Parallel Gradient Descent Algorithm for Person Re-identification

Pedestrian re-identification is a hot topic in computer vision. Convolutional neural network(CNN) has achieved good performance in pedestrian re-identification. However, CNN is computationally intensive because of vast pedestrian data and depth of CNN training. As the requirement of higher accuracy, the training always takes days and even weeks. In this paper, we propose a parallel stochastic gradient descent(SGD) algorithm, where five-hierarchy parallel structure sets up blocks based on pedestrian attributes. Moreover, the interval for updating parameters is analyzed to optimize parameter selections. Momentum-combined adaptive learning rate is also adopted. Our results show that this method successfully speeds up the training process by five times and surpasses state-of-the-art in accuracy as well.