文摘
We study a notion of object relevance, as measured in a spatio-temporal context of driving a vehicle. Various spatio-temporal object and scene cues are analyzed for the task of object importance classification. Human-centric metrics are employed for evaluating object detection and studying data bias. Importance-guided training of object detectors is proposed, showing significant improvement over an importance-agnostic baseline.