A web-based tool for fast instance-level labeling of videos and the creation of spatiotemporal media fragments

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• 作者：Anastasia Ioannidou ; Evlampios Apostolidis…
• 关键词：Instance ; level video labeling ; Object re ; detection ; BRISK descriptor ; Locality Sensitive Hashing
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：2
• 页码：1735-1774
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

This paper presents a web-based interactive tool for time-efficient instance-level spatiotemporal labeling of videos, based on the re-detection of manually selected objects of interest that appear in them. The developed tool allows the user to select a number of instances of the object that will be used for annotating the video via detecting and spatially demarcating it in the video frames, and provide a short description about the selected object. These instances are given as input to the object re-detection module of the tool, which detects and spatially demarcates re-occurrences of the object in the video frames. The video segments that contain detected instances of the given object can be then considered as object-related media fragments, being annotated with the user-provided information about the object. A key component for building such a tool is the development of an algorithm that performs the re-detection of the object throughout the video frames. For this, the first part of this work presents our study on different approaches for object re-detection and the finally developed one, which combines the recently proposed BRISK descriptors with a descriptor matching strategy that relies on the LSH algorithm. Following, the second part of this work is dedicated to the description of the implemented tool, introducing the supported functionalities and demonstrating its use for object-specific labeling of videos. A set of experiments and a user study regarding the efficiency of the introduced object re-detection method and the performance of the developed tool indicate that the proposed framework can be used for accurate and time-efficient instance-based annotation of videos, and the creation of object-related spatiotemporal media fragments.