Improving person detection using synthetic training data

Person detection in complex real-world scenes is a challenging problem. State-of-the-art methods typically use supervised learning relying on significant amounts of training data to achieve good detection results. However, labeling training data is tedious, expensive, and error-prone. This paper presents a novel method to improve detection performance by supplementing real-world data with synthetically generated training data. We consider the case of detecting people in crowded scenes within an AdaBoost-framework employing Haar and Histogram-of-Oriented-Gradients (HOG) features. Our evaluations on real-world video sequences of crowded scenes with significant occlusions show that the combination of real and synthetic training data significantly improves overall detection results.