Soccer highlights detection and recognition using HMMs

Assfalg, J.; Bertini, Marco; Bimbo, Alberto Del; Nunziati, W.; Pala, Pietro

doi:10.1109/icme.2002.1035909

Cited by 99 publications

(50 citation statements)

References 8 publications

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“…Since sports game data are generally sequential, and since HMM is a general model for capturing properties of sequential data, many of the papers on sports event detection are based on HMM [10][11][12][13][14][15][16][17]. Some of them use the maximum likelihood method where the Baum-Welch algorithm [18] is used for parameter estimation [19].…”

Section: Related Workmentioning

confidence: 99%

Bayesian event detection for sport games with hidden Markov model

Motoi

Misu²,

Nakada

et al. 2011

Pattern Anal Applic

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Event detection can be defined as the problem of detecting when a target event has occurred, from a given data sequence. Such an event detection problem can be found in many fields in science and engineering, such as signal processing, pattern recognition, and image processing. In recent years, many data sequences used in these fields, especially in video data analysis, tend to be high dimensional. In this paper, we propose a novel event detection method for high-dimensional data sequences in soccer video analysis. The proposed method assumes a Bayesian hidden Markov model with hyperparameter learning in addition to the parameter leaning. This is in an attempt to reduce undesired influences from ineffective components within the high-dimensional data. Implemention is performed by Markov Chain Monte Carlo. The proposed method was tested against an event detection problem with sequences of 40-dimensional feature values extracted from real professional soccer games. The algorithm appears functional.

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Section: Related Workmentioning

confidence: 99%

Bayesian event detection for sport games with hidden Markov model

Motoi

Misu²,

Nakada

et al. 2011

Pattern Anal Applic

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“…The grass-ratio and non-field area distribution are often used as important features in most previous work [1][2] [3][12] [13] to discriminate the shots of Long, Medium, and Close-up view. In [4], they used the moments of colour and shape as features to identify shot types, and modelled the shot temporal transition pattern with a Hidden Markov Model (HMM) to detect semantic events.…”

Section: Introductionmentioning

confidence: 99%

Shot Classification in Broadcast Soccer Video

Tong

Liu²,

Lu³

2008

ELCVIA

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In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness.. IntroductionIn recent years, sports video analysis has received increasing interests due to its tremendous commercial. A popular scheme of sports video analysis has three steps: 1) parsing the video into shots; 2) classifying shots into several types with certain semantics; 3) inferring high-level events with shot transition context. In this paper, we focus on shot classification of soccer video.A lot of work has been done on shot classification in soccer video. The grass-ratio and non-field area distribution are often used as important features in most previous work [1][2][3] [12][13] to discriminate the shots of Long, Medium, and Close-up view. In [4], they used the moments of colour and shape as features to identify shot types, and modelled the shot temporal transition pattern with a Hidden Markov Model (HMM) to detect semantic events. Duan et al [5] extracted shape and motion information to categorize shots with Support Vector Machine (SVM) classifiers. Replay as a special scene of sports video, also plays an importance role in semantic event analysis. H. Pan et al [9] utilized an HMM to infer replay scenes, in which a zero-crossing measure was considered for the frequency and the amplitude of the fluctuations of adjacent frame differences. However, a single frame inside a replay must be pinpointed in advance. Later, they proposed another replay detection method based on replay-logo [14]. Kobla et al [15,16] used the macroblock, motion and bit-rate information in compressed domain to detect replay scenes. Duan et al [17] developed a logo transition detection, in which the mean shift algorithm was used to seek the mood of logo transition, and then a replay scene was determined by a pair of logo-transitions. Wang et al [18] utilized Correspondence to: qsliu@nlpr.ia.ac.cn Recommended for acceptance by Xuelong Li

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“…And the more important advantage of HMM is that it could provide a general method in modeling semantic events in the video. Event detection using HMM have been reported in diverse sport genres including soccer, baseball, tennis, etc [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Semantic Event Detection in Structured Video Using Hybrid HMM/SVM

Bae¹,

Kim²,

Jin³

et al. 2005

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a new semantic event detection algorithm in structured video. A hybrid method that combines HMM with SVM to detect semantic events in video is proposed. The proposed detection method has some advantages that it is suitable to the temporal structure of event thanks to Hidden Markov Models (HMM) and guarantees high classification accuracy thanks to Support Vector Machines (SVM). The performance of the proposed method is compared with that of HMM based method, which shows the performance increase in both recall and precision of semantic event detection.

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Soccer highlights detection and recognition using HMMs

Cited by 99 publications

References 8 publications

Bayesian event detection for sport games with hidden Markov model

Bayesian event detection for sport games with hidden Markov model

Shot Classification in Broadcast Soccer Video

Semantic Event Detection in Structured Video Using Hybrid HMM/SVM

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