Temporal-and Spatial-Driven Video Summarization Using Optimum-Path Forest

Martins, Guilherme Brandão; Papa, João P.; Almeida, Jurandy

doi:10.1109/sibgrapi.2016.053

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Video summarization is a reduced representation for fast video retrieval. In another work [6],a temporal-and spatial-driven approach was proposed. In this study, Optimum-Path Forest (OPF) clustering was used to automatically determine the number of keyframes and extract them to compose the final summary.…”

Section: Related Workmentioning

confidence: 99%

“…Video summarization methods in [1][2][3][4][5][6][7][8][9] select key frames or short clips without analyzing the content of the video can lose information's if the purpose of the summarization is not specified. Also, for these methods the summarization made on the videos which contains different scenes changing during the video periods like movies video.…”

Section: Related Workmentioning

confidence: 99%

“…Action index = Argmin{dist(HOGs training , HOG current )} (6) The distance between histograms used in this paper is the same in (2) and defined in [30]. Here, A and B are two vectors representing histograms, a i and b i are histogram values.…”

Section: Hog Based Recognitionmentioning

confidence: 99%

“…The summarization process can also be related to the type of scene (private or public) where the data analysis depends on whether the scene is dynamic or static, as well as whether it is crowded or uncrowded. Since the summarization process should consume less time for processing and less space for storage it might require pre-processing to enhance the process without losing any information before the feature extraction task [1][2][3][4][5][6][7]. Video summarization methods are generally classified into two main categories: scenebased (static, dynamic) and content-based.…”

Section: Introductionmentioning

confidence: 99%

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A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Hog Based Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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“…To make data analysis easier, make it easier to store information, and make it easier to view videos at any time, a summary of video data is necessary for these systems [6]. The process of summarizing may also be correlated with the type of scene (private or public), where the data analysis is dependent on the scene's dynamic or static nature, as well as its density (whether it is crowded or not) [7]. Since the summarizing method ought to take less time to complete and less storage space, pre-processing can be necessary to improve the process without erasing any data before the feature extraction work [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Digital Video Summarization: A Survey

H. Hendi,

Q. Hussein,

B. Taher

2023

Int J Innov Comp

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Video summarization has arisen as a method that can help with efficient storage, rapid browsing, indexing, fast retrieval, and quick sharing of the material. The amount of video data created has grown exponentially over time. Huge amounts of video are produced continuously by a large number of cameras. Processing these massive amounts of video requires a lot of time, labor, and hardware storage. In this situation, a video summary is crucial. The architecture of video summarization demonstrates how a lengthy film may be broken down into shorter, story-like segments. Numerous sorts of studies have been conducted in the past and continue now. As a result, several approaches and methods—from traditional computer vision to more modern deep learning approaches—have been offered by academics. However, several issues make video summarization difficult, including computational hardware, complexity, and a lack of datasets. Many researchers have recently concentrated their research efforts on developing efficient methods for extracting relevant information from videos. Given that data is gathered constantly, seven days a week, this study area is crucial for the advancement of video surveillance systems that need a lot of storage capacity and intricate data processing. To make data analysis easier, make it easier to store information, and make it easier to access the video at any time, a summary of video data is necessary for these systems. In this paper, methods for creating static or dynamic summaries from videos are presented. The authors provide many approaches for each literary form. The authors have spoken about some features that are utilized to create video summaries.

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