Fast Highlight Detection and Scoring for Broadcast Soccer Video Summarization using On-Demand Feature Extraction and Fuzzy Inference

Sigari, Mohamad Hoseyn; Soltanian‐Zadeh, Hamid; Pourreza, Hamidreza

doi:10.14257/ijcg.2015.6.1.02

Cited by 13 publications

(11 citation statements)

References 23 publications

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“…In high level semantic video analysis, we have a few researchers that use attention control in perceptual space. For example, in [36], a method is presented about fast highlight detection and scoring in soccer videos using on-demand feature extraction. This method uses a bottom-up perceptual attention control in feature space to speed-up the procedure of summarization.…”

Section: Attention Controlmentioning

confidence: 99%

“…In 3DRGB, we calculate 3D RGB histogram of the image. Despite the ordinary RGB histogram, constructed by a concatenation of the histograms of the R, G, and B channels, a 3D RGB histogram is constructed by quantizing the 3D RGB color space and counting the number of pixels in each voxel of the resulting volumetric RGB space [36]. Here, we quantize R, G, and B channels to 4 levels.…”

Section: Pu's At the First Levelmentioning

confidence: 99%

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A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Sigari

Soltanian‐Zadeh

Pourreza

2016

Image and Vision Computing

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Section: Attention Controlmentioning

confidence: 99%

Section: Pu's At the First Levelmentioning

confidence: 99%

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Sigari

Soltanian‐Zadeh

Pourreza

2016

Image and Vision Computing

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“…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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“…The video summary is constructed using Gaussian entropy algorithm [18]. Sigari et al (2015) proposed a fast video summarization using an ondemand feature extraction and a fuzzy inference system. Based on an on-demand feature extraction, the input video is partitioning to highlight and analyze video content.…”

Section: Introductionmentioning

confidence: 99%

“…Each highlight is assigned a score using a Fuzzy Inference System. The score value indicates the importance of the events occurred in the highlight [19]. Bian et al, (2015) proposed a video event summarization method which comprised three stages: (1) noise removal.…”

Section: Introductionmentioning

confidence: 99%

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Al-Musawi¹,

Hasson²

2015

ijacsa

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Abstract-For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.

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Fast Highlight Detection and Scoring for Broadcast Soccer Video Summarization using On-Demand Feature Extraction and Fuzzy Inference

Cited by 13 publications

References 23 publications

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Counterattack detection in broadcast soccer videos using camera motion estimation

Improving Video Streams Summarization Using Synthetic Noisy Video Data

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