| Summary: | Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (<i>VD-NUS</i>). The <i>VD-NUS</i> dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (<i>AAM</i>) to reduce the annotation workload. The proposed <i>AAM</i> framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the <i>AAM</i> framework to help human annotators to rapidly and accurately label the selected data. <i>AAM</i> outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR<sup>−2</sup> on the <i>VD-NUS</i> dataset.
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