SM+: Refined Scale Match for Tiny Person Detection

Detecting tiny objects (e.g., less than 20 × 20 pixels) in large-scale images is an important yet open problem. Modern CNN-based detectors are challenged by the scale mismatch between the dataset for network pre-training and the target dataset for detector training. In this paper, we investigate the scale alignment between pre-training and target datasets, and propose a new refined Scale Match method (termed SM+) for tiny person detection. SM+ improves the scale match from image level to instance level, and effectively promotes the similarity between pre-training and target dataset. Moreover, considering SM+ possibly destroys the image structure, a new probabilistic structure inpainting (PSI) method is proposed for the background processing. Experiments conducted across various detectors show that SM+ noticeably improves the performance on TinyPerson, and outperforms the state-of-the-art detectors with a significant margin.

1. INTRODUCTION

Person detection is an important topic in the computer vision area. It has wide applications including surveillance [1][2], driving assistance [3] and maritime quick rescue [4], etc. The research of detectors [5][6][7][8][9] has achieved significant progress with the rapid development of data-driven deep convolutional neural networks (CNNs). However, the detectors perform poorly when detecting tiny objects with few pixels (e.g., less than 20 × 20 pixels), such as traffic signs [10], persons in aerial images [4], etc.