Content-Aware Summarization of Broadcast Sports Videos: An Audio–Visual Feature Extraction Approach

Khan, Abdullah; Shao, Jie; Ali, Waqar; Tumrani, Saifullah

doi:10.1007/s11063-020-10200-3

Cited by 38 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For fans, they want to be able to watch the full match directly from the event they are interested in. For video editors, they want to be able to quickly organise the video content they need from a large number of sources, or even automatically generate video summaries or highlight reels [16]. What is more, for athletes and coaches alike, the analysis of players' movement data from video footage of football matches is one of the most important tasks in order to train better [17].…”

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

View full text Add to dashboard Cite

In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Since the middle of last century, the main reason why major cities in the world bid for sports events is that the city authorities have seen that holding sports events can temper and enhance the administrative governance ability of the host city government or the host country government, thus being conducive to enhancing the status and reputation of the host city and expanding the popularity of the city. For example, the city of Atlanta is known as a world sports center city for its advanced sports facilities, and Shanghai is accelerating the pace of building a first-class sports center city in Asia [1]. Besides promoting the political influence of the city, the holding of sports events also brings great economic effects to the host city and establishes a good self-renewal mechanism and perfect internal coordination mechanism.…”

Section: Introductionmentioning

confidence: 99%

Research on Evaluation Method of Sports Events Based on Edge Algorithm

Deng

Gudamu

2021

Security and Communication Networks

View full text Add to dashboard Cite

In view of the high computational cost and long computational time of IoT edge algorithm in traditional sports event evaluation, this paper optimizes IoT edge algorithm by introducing deep reinforcement learning technology. Set the IoT edge algorithm cycle through the IoT topology to obtain the data upload speed. In order to improve the evaluation efficiency of sports events, the process of edge algorithm is designed. The contribution rate of evaluation index is calculated, and the consistency, minimum deviation, and minimum difference of the results are taken as the standard to design the evaluation method of sports events. In order to verify the performance of the optimized edge algorithm, the test data set and test platform are set up and the comparative experiment is designed. Compared with the traditional methods, the edge algorithm based on DSLL has lower computational cost, shorter computational time, higher evaluation accuracy, and more practical results.

show abstract

“…Such networks have achieved higher performance in many Artificial Intelligence (AI) applications. AI based automation has already proven itself in other domains [11][12][13].…”

Section: Proposed Methodsmentioning

confidence: 99%

H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Khan

Shafiq

Kumar

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

Self Cite

View full text Add to dashboard Cite

With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.

show abstract

Content-Aware Summarization of Broadcast Sports Videos: An Audio–Visual Feature Extraction Approach

Cited by 38 publications

References 43 publications

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Research on Evaluation Method of Sports Events Based on Edge Algorithm

H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Contact Info

Product

Resources

About