A Study on Keyframe Extraction Methods for Video Summary

Sujatha, C.; Mudenagudi, Uma

doi:10.1109/cicn.2011.15

Cited by 42 publications

(12 citation statements)

References 19 publications

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“…These methods typically divide the video into "shots" [37], [28] by locating key frames corresponding to scene transitions. The search procedure exploits key frame content and matches either low-level descriptors [28] or higher-level semantic meta-tags to a given query [37]. Surveillance videos are fundamentally different than conventional videos.…”

Section: Related Workmentioning

confidence: 99%

Retrieval in Long-Surveillance Videos Using User-Described Motion and Object Attributes

Castañón

Elgharib

Saligrama

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to retrieve video segments, with a focus on detecting objects moving on routes, that match user-defined events of interest. The sheer size and remote locations where surveillance videos are acquired, necessitates highly compressed representations that are also meaningful for supporting user-defined queries. To address these challenges we archive long-surveillance video through lightweight processing based on low-level local spatio-temporal extraction of motion and object features. These are then hashed into an inverted index using locality-sensitive hashing (LSH). This local approach allows for query flexibility as well as leads to significant gains in compression. Our second task is to extract partial matches to the user-created query and assembles them into full matches using Dynamic Programming (DP). DP exploits causality to assemble the indexed low level features into a video segment which matches the query route. We examine CCTV and Airborne footage, whose low contrast makes motion extraction more difficult. We generate robust motion estimates for Airborne data using a tracklets generation algorithm while we use Horn and Schunck approach to generate motion estimates for CCTV. Our approach handles long routes, low contrasts and occlusion. We derive bounds on the rate of false positives and demonstrate the effectiveness of the approach for counting, motion pattern recognition and abandoned object applications.

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Section: Related Workmentioning

confidence: 99%

Retrieval in Long-Surveillance Videos Using User-Described Motion and Object Attributes

Castañón

Elgharib

Saligrama

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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“…Motion feature vector is used to supply a compact video representation while protecting the important actions of the original video [4]. Motion vector is calculated by a motion estimation technique to describe the visual contents with temporal differences inside a video.…”

Section: Introductionmentioning

confidence: 99%

A Meaningful Compact Key Frames Extraction in Complex Video Shots

Mizher

Ang

Mazhar

2017

IJEECS

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Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the the-state-of-the-art algorithms achieved 8.90 in the best case.

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“…Video summarization has been a longstanding research topic and there exist a large amount of work on abstracting the main occurrences, scenes, or objects in a video using a set of automatically extracted keyframes [1]. These approaches are designed for general videos and they seek to find significant scene, object, color, or motion changes in a video.…”

Section: Introductionmentioning

confidence: 99%

Structuring Lecture Videos by Automatic Projection Screen Localization and Analysis

Wang

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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We present a fully automatic system for extracting the semantic structure of a typical academic presentation video, which captures the whole presentation stage with abundant camera motions such as panning, tilting, and zooming. Our system automatically detects and tracks both the projection screen and the presenter whenever they are visible in the video. By analyzing the image content of the tracked screen region, our system is able to detect slide progressions and extract a high-quality, non-occluded, geometrically-compensated image for each slide, resulting in a list of representative images that reconstruct the main presentation structure. Afterwards, our system recognizes text content and extracts keywords from the slides, which can be used for keyword-based video retrieval and browsing. Experimental results show that our system is able to generate more stable and accurate screen localization results than commonly-used object tracking methods. Our system also extracts more accurate presentation structures than general video summarization methods, for this specific type of video.

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A Study on Keyframe Extraction Methods for Video Summary

Cited by 42 publications

References 19 publications

Retrieval in Long-Surveillance Videos Using User-Described Motion and Object Attributes

Retrieval in Long-Surveillance Videos Using User-Described Motion and Object Attributes

A Meaningful Compact Key Frames Extraction in Complex Video Shots

Structuring Lecture Videos by Automatic Projection Screen Localization and Analysis

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