Transferring image-based object detectors to the domain of video remains challenging
under resource constraints. Previous efforts utilised optical flow to allow unchanged features …

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 …

Transferring existing image-based detectors to the video is non-trivial since the quality of
frames is always deteriorated by part occlusion, rare pose, and motion blur. Previous …

For efficient video object detection, our detector consists of a spatial module and a temporal
module. The spatial module aims to detect objects in static frames using convolutional …

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 …

Video object detection enhances the performance of the still-image based object detection
by exploiting temporal context information from neighboring frames. Most of the state-of-the …

The existing methods for video object detection mainly depend on two-stage image object
detectors. The fact that two-stage detectors are generally slow makes it difficult to apply in …

Extending state-of-the-art object detectors from image to video is challenging. The accuracy
of detection suffers from degenerated object appearances in videos, eg, motion blur, video …

We present a new network architecture able to take advantage of spatio-temporal
information available in videos to boost object detection precision. First, box features are …

Recently, one-stage detectors have achieved competitive accuracy and faster speed
compared with traditional two-stage detectors on image data. However, in the field of video …