Localized Object Information from Detected Objects Based on Deep Learning in Video Scene

A. Lee, S. Yong
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引用次数: 1

Abstract

The ultimate goal of computer vision research is to understand a scene semantically from an image or a video. Real-time object detection received significant attention over the past few years. Many challenges remain, especially in the focus of extraction of localized object information for scene representation. In order to have an accurate, intelligent and fast real-time object detection, the implementation of accurate localized information in the machine is inevitable. This research will focus on developing the object localization extractor that can extract the localized object information from the scene for further scene prediction and inference. In particular, (i) our localized extractor can encode significantly high-level features information; (ii) this rich localized information will be used for scene representation and understanding.
基于深度学习的视频场景目标定位方法
计算机视觉研究的最终目标是从图像或视频中理解场景的语义。实时目标检测在过去几年中受到了极大的关注。目前仍存在许多挑战,特别是在场景表示中局部对象信息的提取方面。为了实现准确、智能、快速的实时目标检测,在机器中实现准确的定位信息是必然的。本研究将重点开发目标定位提取器,从场景中提取出定位后的目标信息,用于进一步的场景预测和推理。特别是,(i)我们的局部提取器可以编码显著的高级特征信息;(ii)这些丰富的本地化信息将用于场景表示和理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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