Visualization for neural-network-based person re-identification

Abstract

Given images of a person, person re- identification (Person ReID) techniques aim to find images of the same person from previously collected images. Because of large data sets of person images and the advance of deep learning, convolutional neural networks (CNNs) successfully boost the accuracy of Person ReID algorithms, but it can be difficult to explain and to troubleshoot issues due to the complexity of CNNs. In this paper, we present a visualization-based approach to understand a CNN-based Person ReID algorithm. As Person ReID algorithms are often designed to map images of the same person into similar feature vectors, given two images, we design an algorithm to estimate how much each element in a CNN layer contributes to the similarity between their feature vectors. Based on the estimation, we build a visualization tool to interactively locate and visualize the activation of highly-contributing elements, other than manually examining all. Our visualization tool also supports various user interaction widgets to explore a Person ReID data set, locate difficult cases, and analyze the reason behind their similarities. We show a use case with our tool to understand and troubleshoot issues in a CNN-based Person ReID algorithm.