Automatic Soccer Video Analysis and Summarization

Ekin, Ahmet; Tekalp, A.M.

doi:10.1117/12.476238

Cited by 77 publications

(77 citation statements)

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“…Action anticipation (sometimes referred to as action prediction) is gaining a lot of attention due to its many real world applications such as human-computer interaction [2,33,30], sports analysis [3,4,56] and pedestrian movement prediction [9,21,18,5,46] especially in the autonomous driving scenarios.…”

Section: Introductionmentioning

confidence: 99%

Action Anticipation with RBF Kernelized Feature Mapping RNN

Shi

Fernando

Hartley

2018

Lecture Notes in Computer Science

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We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN . Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. Using only some of the earliest frames of a video, the feature mapping RNN is able to generate future features with a fraction of the parameters needed in traditional RNN. By feeding these future features into a simple multilayer perceptron facilitated with an RBF kernel layer, we are able to accurately predict the action in the video.In our experiments, we obtain 18% improvement on JHMDB-21 dataset, 6% on UCF101-24 and 13% improvement on UT-Interaction datasets over prior stateof-the-art for action anticipation.

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Section: Introductionmentioning

confidence: 99%

Action Anticipation with RBF Kernelized Feature Mapping RNN

Shi

Fernando

Hartley

2018

Lecture Notes in Computer Science

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“…Label Estimation of Internet Videos We pre-process Internet videos for training labels. Given a raw video, we first detect shot boundaries using [13]. We call the consecutive frames at the shot boundary boundary frames for simplicity.…”

Section: Methodsmentioning

confidence: 99%

Learning Sports Camera Selection From Internet Videos

Chen

Tian

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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This work addresses camera selection, the task of predicting which camera should be "on air" from multiple candidate cameras for soccer broadcast. The task is challenging because of the scarcity of learning data with all candidate views. Meanwhile, broadcast videos are freely available on the Internet (e.g. Youtube). However, these videos only record the selected camera views, omitting the other candidate views. To overcome this problem, we first introduce a random survival forest (RSF) method to impute the incomplete data effectively. Then, we propose a spatialappearance heatmap to describe foreground objects (e.g. players and balls) in an image. To evaluate the performance of our system, we collect the largest-ever dataset for soccer broadcasting camera selection. It has one main game which has all candidate views and twelve auxiliary games which only have the broadcast view. Our method significantly outperforms state-of-the-art methods on this challenging dataset. Further analysis suggests that the improvement in performance is indeed from the extra information from auxiliary games.

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“…Human action recognition is an active research area that has great importance in multiple domains [7,9,23]. Since the inception of the field researchers have focused on improving the applicability of methods to tally with real world scenarios.…”

Section: Previous Workmentioning

confidence: 99%

Predicting the Future: A Jointly Learnt Model for Action Anticipation

Gammulle

Denman

Sridharan³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future. In contrast to current state-of-the-art methods which first learn a model to predict future video features and then perform action anticipation using these features, the proposed framework jointly learns to perform the two tasks, future visual and temporal representation synthesis, and early action anticipation. The joint learning framework ensures that the predicted future embeddings are informative to the action anticipation task. Furthermore, through extensive experimental evaluations we demonstrate the utility of using both visual and temporal semantics of the scene, and illustrate how this representation synthesis could be achieved through a recurrent Generative Adversarial Network (GAN) framework. Our model outperforms the current state-of-the-art methods on multiple datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction. 1

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Automatic Soccer Video Analysis and Summarization

Cited by 77 publications

References 0 publications

Action Anticipation with RBF Kernelized Feature Mapping RNN

Action Anticipation with RBF Kernelized Feature Mapping RNN

Learning Sports Camera Selection From Internet Videos

Predicting the Future: A Jointly Learnt Model for Action Anticipation

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