Abstract

Instrumented vehicles can produce huge volumes of video data per vehicle per day that must be analysed automatically, often in real time. This analysis should include identifying the presence of objects and tagging these as semantic concepts such as car, pedestrian, etc. An important element in achieving this is the annotation of training data for machine learning algorithms, which requires accurate labels at a high-level of granularity. Current practise is to use trained human annotators who can annotate only a limited volume of video per day. In this paper, we demonstrate how a generic human saliency classifier can provide visual cues for object detection using deep learning approaches. Our work is applied to datasets for autonomous driving. Our experiments show that utilizing visual saliency improves the detection of small objects and increases the overall accuracy compared with a standalone single shot multibox detector.