Video object detection is the task of detecting objects from a video as opposed to images.
( Image credit: Learning Motion Priors for Efficient Video Object Detection )
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Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences.
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Ranked #2 on Video Object Detection on ImageNet VID
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.
In this paper, we present a light weight network architecture for video object detection on mobiles.
Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors.