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Video object detection (VID) has been vigorously studied for years but almostall literature adopts a static accuracy-based evaluation, i.e., averageprecision (AP). From a robotic perspective, the importance of recall continuityand localization stability is equal to that of accuracy, but the AP isinsufficient to reflect detectors' performance across time. In this paper,non-reference assessments are proposed for continuity and stability based onobject tracklets. These temporal evaluations can serve as supplements to staticAP. Further, we develop an online tracklet refinement for improving detectors'temporal performance through short tracklet suppression, fragment filling, andtemporal location fusion.  In addition, we propose a small-overlap suppression to extend VID methods tosingle object tracking (SOT) task so that a flexible SOT-by-detection frameworkis then formed.  Extensive experiments are conducted on ImageNet VID dataset and real-worldrobotic tasks, where the superiority of our proposed approaches are validatedand verified. Codes will be publicly available.

Rethinking Temporal Object Detection from Robotic Perspectives