Soccer Video Research Articles

Research on Data Capture Technology in Soccer Videos

Research on Data Capture Technology in Soccer Videos

The Key Events Extraction Algorithm Based on Shot Events in Soccer Video

The Key Events Extraction Algorithm Based on Shot Events in Soccer Video

Camera Lens Detection Algorithm based on the Dominant Color Image in Soccer Video

Camera Lens Detection Algorithm based on the Dominant Color Image in Soccer Video

6. Visualization Methods for Encourage Experience of Sports in Soccer Videos

6. Visualization Methods for Encourage Experience of Sports in Soccer Videos

Detection of Corner Event Based on Hidden Markov Model in Soccer Video

Detection of Corner Event Based on Hidden Markov Model in Soccer Video

Research on Analysis of Sports Video based on the Statistics

We turn to statistical-based methods and propose a statistical inference approach by using Dynamic Bayesian Network, which is able to learn automatically from data set. By soccer video analysis is as an example, the proposed method is verified by experiment. We extract the color, shape and other low-level features from the video, to detect and identify 5 kinds of high-level semantic events using dynamic Bayesian network model. The experimental results show that our method is effective.

Analysis of Offensive Strategy in Soccer Video

A novel Real-world trajectory based tactic analysis method is proposed, which can automatically analyze the offensive tactics of soccer game from the perspective of professionals. First, a real-world trajectory extraction method is proposed, and then an offensive pattern recognition method is described based on the definition of the ball states. Our experiments on user study by soccer professionals demonstrate that the defined offensive patterns can be used to analyze soccer tactics effectively in terms of conciseness, clarity and usability.

Multi-Mode Semantic Cues Based on Hidden Conditional Random Field in Soccer Video

Multi-Mode Semantic Cues Based on Hidden Conditional Random Field in Soccer Video

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Current semantic video analysis systems are usually hierarchical and consist of some levels to overcome semantic gaps between low-level features and high-level concepts. In these systems, some features, descriptors, objects or concepts are extracted in each level and therefore, total computational complexity of such systems is huge. In this paper, we present a new general framework to impose attention control on a video analysis system using Q-learning. Thus, our proposed framework restructures a given system dynamically to direct attention to the blocks extracting the most informative features/concepts and reduces computational complexity of the system. In other words, the proposed framework directs flow of processing actively using a learning attention control method. The proposed framework is evaluated for event detection in broadcast soccer videos using limited numbers of training samples. Experiments show that the proposed framework is able to learn how to direct attention to informative features/concepts and restructure the initial structure of the system dynamically to reach the final goal with less computational complexity.

Research on Effective Field Lines Detection and Tracking Algorithm in Soccer Videos

A novel field line detection method is proposed based on the directional information of field line. First, the special character of modern soccer playfield is considered, and a novel soccer field model is introduced which includes offside assistant lines. By designing a directional filter, these field lines can be accurately detected. Moreover, the field line detection and tracking for a clip is developed, which can further improve the performance of field line detection. In this paper, we integrate the basic theories of multimedia analysis, pattern recognition and computer vision into content analysis for sports video analysis. Through exploring several components such as object detection, camera calibration and tactic analysis, a systematical system is built up. The proposed algorithms can be not only used for soccer video analysis but also used for other sports video analysis. It provides a promising method for general multimedia analysis.

Automatic summarization of soccer highlights using audio-visual descriptors.

Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach.

3D-Multiple Object Tracking training task improves passing decision-making accuracy in soccer players

ObjectivesThe ability to perform a context-free 3-dimensional multiple object tracking (3D-MOT) task has been highly related to athletic performance. In the present study, we assessed the transferability of a perceptual-cognitive 3D-MOT training from a laboratory setting to a soccer field, a sport in which the capacity to correctly read the dynamic visual scene is a prerequisite to performance. DesignThroughout pre- and post-training sessions, we looked at three essential skills (passing, dribbling, shooting) that are used to gain the upper hand over the opponent. MethodWe recorded decision-making accuracy during small-sided games in university-level soccer players (n = 23) before and after a training protocol. Experimental (n = 9) and active control (n = 7) groups were respectively trained during 10 sessions of 3D-MOT or 3D soccer videos. A passive control group (n = 7) did not received any particular training or instructions. ResultsDecision-making accuracy in passing, but not in dribbling and shooting, between pre- and post-sessions was superior for the 3D-MOT trained group compared to control groups. This result was correlated with the players' subjective decision-making accuracy, rated after pre- and post-sessions through a visual analogue scale questionnaire. ConclusionsTo our knowledge, this study represents the first evidence in which a non-contextual, perceptual-cognitive training exercise has a transfer effect onto the field in athletes.

Bayesian Network-Based Customized Highlight Generation for Broadcast Soccer Videos

Sports highlight generation techniques aim at condensing a full-length video to a significantly shortened version that still preserves the main interesting content of the original video. In this paper, we present the system for automatically generating the highlights from sports TV broadcasts. The proposed system detects exciting clips based on audio features and then classify the individual scenes within the clip into events such as replay, player, referee, spectator, and players gathering. A probabilistic Bayesian belief network based on observed events is used to assign semantic concept-labels to the exciting clips, such as goals, saves, yellow-cards, red-cards, and kicks in soccer video sequences. The labeled clips are selected according to their degree of importance to include in the highlights. We have successfully generated highlights from soccer video sequences.

Anterior Cruciate Ligament Injury.

Anterior Cruciate Ligament Injury.

Real-time segmentation methods for monocular soccer videos

The analysis of video records of soccer games is a topic of extensive and challenging research in image sequence processing, particularly that of player and field tracking. In this paper, we propose a new approach for detecting players and field lines in monocular TV video data that involves determining the convex field area and grass colors. This is carried out by considering contextual knowledge, together with a new method for color segmentation that selects polyhedrons in a frame-wise manner within the RGB cube. In summary, for every input image, a binary mask of the field area and a background mask are determined. Both mask creation algorithms achieve per pixel F1 scores of about 98% within our representative test set and are real-time capable. Applications like line detection and player tracking are presented.

Research on Camera Calibration in Football Broadcast Videos

A novel continuous camera calibration algorithm is proposed, which can address the calibration for non-goal area of soccer video. By considering the fact that the position of main camera is stable, a novel continuous camera calibration algorithm is proposed for soccer video analysis. There are two stages for calibration: the first stage addresses the calibration only for goal area and the position of camera is computed and recorded, and then the second stage can address calibration for any area including non-goal area where the camera position is given at first stage. Thus, the proposed algorithm can address the calibration for non-goal area of soccer video, which cannot be addressed by traditional calibration algorithms since the inadequate of calibration objects.

Ball detection in soccer images using isophote’s curvature and discriminative features

Ball recognition in soccer matches is a critical issue for automatic soccer video analysis. Unfortunately, because of the difficulty in solving the problem, many efforts of numerous researchers have still not produced fully satisfactory results in terms of accuracy. This paper proposes a ball recognition approach that introduces a double level of innovation. Firstly, a randomized circle detection approach based on the local curvature information of the isophotes is used to identify the edge pixels belonging to the ball boundaries. Then, ball candidates are validated by a learning framework formulated into a three-layered model based on a variation of the conventional local binary pattern approach. Experimental results were obtained on a significant set of real soccer images, acquired under challenging lighting conditions during Italian "Serie A" matches. The results have been also favorably compared with the leading state-of-the-art methods.

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

In today's world, the rapid development of digital video and editing technology has led to fast growing of video data, creating the need for effective and advanced techniques for analysis and video retrieval, as multimedia repositories have made browsing, delivery of contents (video) and video retrieval very slow. Hence, video summarization proposes various ways for faster browsing among a large amount of data and also for content indexing. Many people spend their free time to watch or play different sports like soccer, cricket, etc. but it is not possible to watch each and every game due to the longer timing of the game. In such cases, the users may just want to view the summary of the video that is just an abstract of the original video, instead of watching the whole video that provides more information about the occurrence of various incidents in the video. It is preferable to watch just highlights of the game or just review/trailer of a movie. Apparently, summarizing a video is an important process. In this paper, video summarization approaches are discussed, that can generate static or dynamic summaries. We present different techniques for each mode in literature. We have discussed some features used for generating video summaries. As soccer is the world’s most famous game played and watched, it is taken as a case study. Research done in this domain is discussed. We conclude that there is a broad perspective for further research in this field.

Goal Detection in Soccer Video: Role-Based Events Detection Approach

Soccer video processing and analysis to find critical events such as occurrences of goal event have been one of the important issues and topics of active researches in recent years. In this paper, a new role-based framework is proposed for goal event detection in which the semantic structure of soccer game is used. Usually after a goal scene, the audiences’ and reporters’ sound intensity is increased, ball is sent back to the center and the camera may: zoom on Player, show audiences’ delighting, repeat the goal scene or display a combination of them. Thus, the occurrence of goal event will be detectable by analysis of sequences of above roles. The proposed framework in this paper consists of four main procedures: 1- detection of game’s critical events by using audio channel, 2- detection of shot boundary and shots classification, 3- selection of candidate events according to the type of shot and existence of goalmouth in the shot, 4- detection of restarting the game from the center of the field. A new method for shot classification is also presented in this framework. Finally, by applying the proposed method it was shown that the goal events detection has a good accuracy and the percentage of detection failure is also very low. DOI: http://dx.doi.org/10.11591/ijece.v4i6.6377

REVIEW ON CONTENT BASED VIDEO LECTURE RETRIEVAL

Recent advances in multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. Furthermore, the new possibilities offered by the information highways have made a large amount of video data publicly available. However, without appropriate search techniques all these data are hardly usable. Users are not satisfied with the video retrieval systems that provide analogue VCR functionality. For example, a user analyses a soccer video will ask for specific events such as goals. Content-based search and retrieval of video data becomes a challenging and important problem. Therefore, the need for tools that can be manipulate the video content in the same way as traditional databases manage numeric and textual data is significant. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This project presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition on lecture audio tracks.