Flexible soccer video summarization in compressed domain

Kiani, Vahid; Pourreza, Hamid Reza

doi:10.1109/iccke.2013.6682798

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Several of these multimodal approaches were proposed before the so-called "deep learning era", targeting either generic or domain/task-specific video summarization. Some indicative and recent examples can be found in [61], [62], [63], [64], [65], [66], [67], [68]. Addressing the video summarization task from a slightly different perspective, other multimodal algorithms generate a text-based summary of the video [69], [70], [71], and extend this output by extracting one representative key-frame [72].…”

Section: Multimodal Approachesmentioning

confidence: 99%

Video Summarization Using Deep Neural Networks: A Survey

Apostolidis¹,

Adamantidou²,

Metsai³

et al. 2021

Preprint

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Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based analysis pipeline. Then, we suggest a taxonomy of the existing algorithms and provide a systematic review of the relevant literature that shows the evolution of the deep-learning-based video summarization technologies and leads to suggestions for future developments. We then report on protocols for the objective evaluation of video summarization algorithms and we compare the performance of several deep-learning-based approaches. Based on the outcomes of these comparisons, as well as some documented considerations about the suitability of evaluation protocols, we indicate potential future research directions.

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Section: Multimodal Approachesmentioning

confidence: 99%

Video Summarization Using Deep Neural Networks: A Survey

Apostolidis¹,

Adamantidou²,

Metsai³

et al. 2021

Preprint

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“…In a far-or medium-view shot, we expect to have a high Grass R value, while Obj S of far-view shots is greater than medium-view shots [2,4]. These features are fed to a cascade classifier including two linear SVM classifiers for view type classification.…”

Section: B View Type Recognitionmentioning

confidence: 99%

“…Sport video analysis is performed for video classification [1], video summarization [2,3,4,5], video retrieval [6,7], team/player tactic analysis [8,9], developing referee-assistant systems [10,11] and etc. Thus, it is demanded by coaches and sport reporters for technical and tactical analysis [8,9] or statistical information of a match (e.g.…”

Section: Introductionmentioning

confidence: 99%

Counterattack detection in broadcast soccer videos using camera motion estimation

Sigari

Soltanian‐Zadeh

Kiani

et al. 2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and mediumview shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.Index Terms-Broadcast soccer video, counterattack detection, camera motion estimation, event detection, video analysis.

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“…A superimposed caption embedded in the video has been used in [16] to detect the scoreboard of the baseball videos. Semantic features along with replays and audio energy have been applied for soccer video summarization in [17]. In [18], rule-based approaches have been used for the detection of events in sports videos.…”

Section: Introductionmentioning

confidence: 99%

A novel framework for semantic analysis of an illumination-variant soccer video

Pandya

Zaveri

2014

J Image Video Proc

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This paper presents an effective, novel and robust framework for semantic analysis of a soccer video possessing varying illumination conditions. The proposed algorithm works in two phases. The proposed framework effectively detects and gathers important events in the first phase, and a later phase carries out the task of event classification. The proposed system aims to identify high-level semantics of a soccer video like card event, goal event, goal attack and other classes of events. The proposed framework effectively exploits optical flow and colour features to detect and classify the events. The use of event filtration and categorization features successfully makes the system effective over various conditions. Simulations have been performed on a large number of video datasets of different conditions of various soccer leagues. Simulation results reflect the efficiency and robustness of the proposed framework.

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Flexible soccer video summarization in compressed domain

Cited by 9 publications

References 22 publications

Video Summarization Using Deep Neural Networks: A Survey

Video Summarization Using Deep Neural Networks: A Survey

Counterattack detection in broadcast soccer videos using camera motion estimation

A novel framework for semantic analysis of an illumination-variant soccer video

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