Adapting ObjectBox for accurate hand detection

Abstract

Hand detection plays a crucial role in various computer vision applications, yet it has received limited research focus in recent years, lagging behind the generic object detection. In this work, we present HandBox to address this gap. HandBox leverages the capabilities of the advanced one-stage anchor-free object detector ObjectBox for accurate hand detection, in which we first scrutinize the limitations and shortcomings of ObjectBox in localizing small objects such as hands and subsequently put forward targeted remedies to enhance its performance. Experiments on two datasets, namely the Oxford-Hand dataset and the Contact-Hand dataset, show that HandBox outperforms ObjectBox by a large margin and achieves 86.21% and 87.79% ${\text{AP}}_{50}$ respectively, setting a new benchmark for hand detection. Experiments on the MSCOCO dataset also showcase that our reformed HandBox is able to achieve better performance on generic object detection against ObjectBox, especially on detecting small objects. Codes will be made public at https://github.com/HandDetector/HandBox.