Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning A Case Study on Object Tracking Tasks

Abstract

Obtaining training data for machine learning models can be challenging. Capturing or gathering the data, followed by its manual labelling, is an expensive and time-consuming process. In cases where there are no publicly accessible datasets, this can significantly hinder progress. In this paper, we analyze the similarity between synthetic and real data. While focusing on an object tracking task, we investigate the quantitative improvement influenced by the concentration of the synthetic data and the variation in the distribution of training samples induced by it. Through examination of three well-known benchmarks, we reveal guidelines that lead to performance gain. We quantify the minimum variation required and demonstrate its efficacy on prominent object-tracking neural network architecture.