Improving person re-identification based on two-stage training of convolutional neural networks and augmentation

Abstract

Objectives. The main goal is to improve person re-identification accuracy in distributed video surveillance systems.

Methods. Machine learning methods are applied.

Result. A technology for two-stage training of convolutional neural networks (CNN) is presented, characterized by the use of image augmentation for the preliminary stage and fine tuning of weight coefficients based on the original images set for training. At the first stage, training is carried out on augmented data, at the second stage, fine tuning of the CNN is performed on the original images, which allows minimizing the losses and increasing model efficiency. The use of different data at different training stages does not allow the CNN to remember training examples, thereby preventing overfitting.

Proposed method as expanding the training sample differs as it combines an image pixels cyclic shift, color  exclusion and fragment replacement with a reduced copy of another image. This augmentation method allows to get a wide variety of training data, which increases the CNN robustness to occlusions, illumination, low image resolution, dependence on the location of features.

Conclusion. The use of two-stage learning technology and the proposed data augmentation method made it possible to increase the person re-identification accuracy for different CNNs and datasets: in the Rank1 metric  by 4–21 %; in the mAP by 10–31 %; in the mINP by 39–60 %.