Event detection in soccer videos using unsupervised learning of Spatio-temporal features based on pooled spatial pyramid model

Fakhar, Babak; Kanan, Hamidreza Rashidy; Behrad, Alireza

doi:10.1007/s11042-018-7083-1

Cited by 30 publications

(16 citation statements)

References 60 publications

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“…Earlier approaches for the automated detection of selected events have employed probabilistic models such as Bayesian Networks [48,49], and hidden Markov model (HMM) [50][51][52][53][54]. With the rise of machine learning, there has been growing interest in using support vector machine (SVM) [55][56][57][58][59][60] and deep learning [45,[61][62][63][64][65] due to their relatively higher performance (detection accuracy) and the availability of more advanced computing infrastructures.…”

Section: Event Detection In Soccer Videosmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

Stige

Hicks

et al. 2021

MAKE

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Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.

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Section: Event Detection In Soccer Videosmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

Stige

Hicks

et al. 2021

MAKE

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“… Tracking Data: It is a spatio-temporal dataset [17]. This dataset will contain X and Y coordinates of the players on the field as well as the ball, with respect to the centre of the football pitch  Event Data: This is also a spatio-temporal dataset [9]. It contains information about the various events like pass, shoot and tackle that occurs during the game.…”

Section: B Player Evaluationmentioning

confidence: 99%

Football Analysis: An Automated System for Team Generation

Desai¹,

Tapiawala²,

Ram³

2021

New Frontiers in Communication and Intelligent Systems

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Football Analytics has seen major growth in the past couple of years. Many of the top football clubs have started implementing various aspects of the vast field of football analytics. The utilization of data has become particularly important with respect to player development. In such a fast-paced game, keeping track of all the players at once is an extremely difficult task. In the paper, we propose the idea of an automated system that will help the manager to pick out the team strategy, with the playing line-up, for a particular match depending on the opponent’s team tactics. The system will include 2 phases, first phase will predict the player’s playing position using cluster analysis. In the next phase, the system will use deep learning to evaluate the performance of players based on their position. Using the output from the two phases, we will predict the team strategies.

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“…This method can scale to more than just a few logos. Fakhar et al [5] use the Apriori algorithm to identify frequent spatial configurations of local features extracted on a spatial pyramid. Here mining requires a large amount of training data and is computationally costly.…”

Section: A Literature Reviewmentioning

confidence: 99%

Well-Known brands recognition by automated classifiers using local and global features

Niaz

Raza

2020

Jurnal Informatika

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Event detection in soccer videos using unsupervised learning of Spatio-temporal features based on pooled spatial pyramid model

Cited by 30 publications

References 60 publications

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Football Analysis: An Automated System for Team Generation

Well-Known brands recognition by automated classifiers using local and global features

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