SSET: a dataset for shot segmentation, event detection, player tracking in soccer videos

Feng, Na; Song, Zikai; Yu, Jiguo; Chen, Yi‐Ping Phoebe; Zhao, Yizhu; He, Yansong; Guan, Tao

doi:10.1007/s11042-020-09414-3

Cited by 16 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SSET [205] is three times smaller than SoccerNet, comprising of 350 football match videos, totaling 282 video hours. Similar to ComprehensiveSoccer, the annotations are in three levels: bounding boxes of players, event/story categories and shot categories, but SSET is larger than ComprehensiveSoccer dataset.…”

Section: A Footballmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

View full text Add to dashboard Cite

To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

show abstract

Section: A Footballmentioning

confidence: 99%

“…Basically, large-scale datasets+deep models dominate the field of soccer video action recognition in recent years, increasing the popularity of SoccerNet [24] and SoccerNet-v2 [208]. While SoccerDB [206], SSET [205] and Comprehen-siveSoccer [45] are more feasible for the tasks that require player detection.…”

Section: A Footballmentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It contains 222 broadcast soccer videos with around 170 video hours. Its follow-up work SSET [13] dataset expends the number of videos in [42] from 222 to 350, and increases the events from 6850 to 10619. A soccer player position dataset [34] provided body-sensor traces and corresponding videos captured from three professional soccer games.…”

Section: Benchmarks For Visual Object Trackingmentioning

confidence: 99%

“…There is a small quantity of tracking datasets focused on a soccer player. Due to the small scale and low challenge, the latest tracking algorithms have obtained indistinguishable results on these datasets (eg., [13] and [34]). (2) High similarity between sequences.…”

Section: Introductionmentioning

confidence: 99%

Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

Song

Wan

Yuan

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

Self Cite

View full text Add to dashboard Cite

Player tracking in broadcast soccer videos has received widespread attention in the field of sports video analysis, however, we note that there is not a suitable tracking algorithm specifically for soccer video, and the existing benchmarks used for soccer player tracking cover few scenarios with low difficulties. From the observation of the soccer scene that interference and occlusion are knotty problems because the distractors are extremely similar to the targets, a distractor-aware player tracking algorithm and a high-quality benchmark for soccer play tracking (BSPT) have been presented. The distractor-aware player tracking algorithm is able to perceive semantic information about distracting players in the background by similarity judgment, the semantic distractor-aware information is encoded into a context vector and is constantly updated as the objects move through a video sequence. Distractor-aware information is then appended to the tracking result of the baseline tracker to improve the intra-class discriminative power. BSPT contains a total of 120 sequences with rich annotations. Each sequence covers 8 specialized frame-level attributes from soccer scenarios and the player occlusion situations are finely divided into 4 categories for a more comprehensive comparison. In the experimental section, the performance of our algorithm and the other 14 compared trackers are evaluated on BSPT with detailed analysis. Experimental results reveal the effectiveness of the proposed distractor-aware model especially under the attribute of occlusion. The BSPT benchmark and raw experimental results are available on the project page at http://media.hust.edu.cn/BSPT.htm.

show abstract

“…The availability of problem-related datasets greatly helps the researchers to train and evaluate their proposed systems without devoting considerable efforts on generating new datasets. However, to the best of our knowledge, there are very few soccer videos datasets [17][18][19], and further the existing datasets are very specific and do not cover salient events of soccer.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

Muhammad

Ullah

Obaidat

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

The salient events recognition of soccer matches in next-generation Internet of things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an Artificial Intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players' performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports' managerial staff as a financial technology (FinTech) service. In this paper, we propose an efficient deep learning-based framework for multiperson salient soccer events recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: Firstly, image frames are extracted from video streams and resized in the preprocessing step to match the input of the deep network. Secondly, frame-level discriminative features are extracted using a pre-trained convolutional neural network (CNN) architecture. Thirdly, we employ a multi-layer long short-term memory (MLSTM) network to recognize high-level events in soccer videos by exploiting the sequential relation between adjacent frames. Moreover, we introduce a new soccer video events (SVE) dataset containing videos of six salient events of soccer game. To provide a strong baseline, we evaluate our newly created SVE dataset using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient events recognition in Nx-IoT environments.

show abstract

SSET: a dataset for shot segmentation, event detection, player tracking in soccer videos

Cited by 16 publications

References 62 publications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

Contact Info

Product

Resources

About