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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The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
#4 best model for Video Object Detection on ImageNet VID
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
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.
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.