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.
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors.
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.
#2 best model for Video Object Detection on ImageNet VID