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首页> 外文期刊>Journal of visual communication & image representation >Quality-guided key frames selection from video stream based on object detection
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Quality-guided key frames selection from video stream based on object detection

机译：基于目标检测的视频流中质量指导的关键帧选择

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摘要

Object detection technique is widely applied in modern intelligent systems, such as pedestrian tracking, video surveillance. Key frames selection aims to select more informative frames and reduce amount of redundant information frames. Traditional methods leveraged SIFT feature, which have high key frame selection error rate. In this paper, we propose a novel key frames selection method based on object detection and image quality. Specifically, we first leverage object detector to detect object, such as pedestrian, vehicles. Then, each training frame will be assigned with a quality score, where frames contain objects have high quality score. Afterwards, we leverage CNN based AlexNet architecture for deep feature representation extraction. Our algorithm combines mutual information entropy and SURF image local features to extract key frames. Comprehensive experiments verify the feasibility of practicing the key frame extractor based on convolutional neural network by training the model, and conduct a quality assessment model study. (C) 2019 Elsevier Inc. All rights reserved.
机译：目标检测技术被广泛应用于现代智能系统中，例如行人跟踪，视频监控。关键帧选择旨在选择更多的信息帧并减少冗余信息帧的数量。传统方法利用了SIFT功能，该功能具有较高的关键帧选择错误率。本文提出了一种基于目标检测和图像质量的关键帧选择方法。具体而言，我们首先利用对象检测器来检测诸如行人，车辆之类的对象。然后，将为每个训练框架分配一个质量得分，其中框架包含具有高质量得分的对象。之后，我们利用基于CNN的AlexNet架构进行深度特征表示提取。我们的算法结合了互信息熵和SURF图像局部特征来提取关键帧。综合实验通过训练模型验证了基于卷积神经网络实践关键帧提取器的可行性，并进行了质量评估模型研究。 （C）2019 Elsevier Inc.保留所有权利。

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