Scale Match for Tiny Person Detection

Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han

Abstract: Visual object detection has achieved unprecedented advance with the rise of deep convolutional neural networks. However, detecting tiny objects (for example tiny persons less than 20 pixels) in large-scale images remains not well investigated. The extremely small objects raise a grand challenge about feature representation while the massive and complex backgrounds aggregate the risk of false alarms. In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising direction for tiny object detection in a long distance and with massive backgrounds. We experimentally find that the scale mismatch between the dataset for network pre-training and the dataset for detector learning could deteriorate the feature representation and the detectors. Accordingly, we propose a simple yet effective Scale Match approach to align the object scales between the two datasets for favorable tiny-object representation. Experiments show the significant performance gain of our proposed approach over state-of-the-art detectors, and the challenging aspects of TinyPerson related to real-world scenarios. The TinyPerson benchmark and the code for our approach will be publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .