Video Object Detection
42 papers with code • 4 benchmarks • 6 datasets
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 )
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
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.
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
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.