Video object detection is a challenging task due to the presence of appearance deterioration
in video frames. Recently, feature aggregation based methods which aggregate context …

Intelligent unmanned aerial vehicles (UAVs) are drawing more and more attention from
industry to academia. UAV navigation plays an important role in the cooperative scenario …

Single-frame object detectors perform well on videos sometimes, even without temporal
context. However, challenges such as occlusion, motion blur, and rare poses of objects are …

Single shot detectors that are potentially faster and simpler than two-stage detectors tend to
be more applicable to object detection in videos. Nevertheless, the extension of such object …

Video object detection is challenging and essential in practical applications, such as
surveillance cameras for traffic control and public security. Unlike the video in natural …

Video object detection is a tough task due to the severe appearance degradation caused by
rapid motion, sudden occlusion or rare poses. The great challenge facing video object …

Average precision (AP) is a widely used metric to evaluate detection accuracy of image and
video object detectors. In this paper, we analyze the object detection from video and point …

This paper proposes an end-to-end deep learning framework, termed as motion-aid feature
calibration network (MFCN), for video object detection. The key idea is to leverage on the …

Transferring image-based object detectors to the domain of videos remains a challenging
problem. Previous efforts mostly exploit optical flow to propagate features across frames …

Abstract In Video Object Detection (VID), a common practice is to leverage the rich temporal
contexts from the video to enhance the object representations in each frame. Existing …