ロボティクス視点で見直す時系列物体検出
Rethinking Temporal Object Detection from Robotic Perspectives
動画物体検出の評価に追従性と位置安定性の指標を導入し、オンラインでのトラックレット補正や単一物体追跡への拡張を提案した論文。
著者: Xingyu Chen, Zhengxing Wu, Junzhi Yu, Li Wen
分類: cs.CV, cs.RO
原文アブストラクト
Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available.
