Adaptive Brightness Learning for Active Object Recognition
Xu, Nuo1,2; Huo, Chunlei1,2; Pan, Chunhong1,2
2019
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Conference Date2019.5.12-5.17
Conference PlaceBrighton, UK
Abstract

State-of-the-art object detection methods based on deep learning achieved promising performances in recent years. However, the performances are limited by the passive nature of the traditional object recognition framework in ignoring the relationship between imaging configuration and recognition performance as well as the importance of recognition performance feedback for improving image quality. To address the above limitations, an active object recognition method based on reinforcement learning is proposed in this paper by taking adaptive brightness adjustment as an example. Progressive brightness adjustment strategy is learned by maximizing recognition performance on reference high-quality training samples. With the help of active object recognition and brightness adjustment strategy, low-quality images can be converted into high-quality images, and overall performances are improved without retraining the detector.

Language英语
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory视觉信息处理
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50610
Collection多模态人工智能系统全国重点实验室_先进时空数据分析与学习
Corresponding AuthorHuo, Chunlei
Affiliation1.NLPR, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Nuo,Huo, Chunlei,Pan, Chunhong. Adaptive Brightness Learning for Active Object Recognition[C],2019.
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