Broadcast Soccer Video Research Articles

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.

A Survey on Object Detection and Tracking in Soccer Videos

"The players and ball are the most important object in soccer game videos and detected them are a challenging task because of many difficulties, such as shadow and illumination, ball size, several other objects look like a ball, often the ball overlapping with players or merged with lines, as well as the ball may be disappear which be hidden in the stadium or flying on air, and similar appearance of players, etc. The detect ball is the first step for tracking in broadcast soccer video. There are several methods of ball-tracking are based on their problem. In this paper, we have discussed different methods of object detection and tracking in the soccer videos which are available in the literature.

A fog based ball tracking (FB2T) system using intelligent ball bees

Soccer is the most popular group game; it has a large number of fans all over the world. Although each complete soccer match lasts around one hour and a half, only have very few scenes attracting audiences been perfectly filmed. This filming has usually been done using static (fixed) cameras, and they cannot provide the same level of accuracy or entertainment given by mobile devices. Taking into our consideration that there are no schemes using mobile devices have been proposed to solve this problem till now. As periodic communication generates a huge amount of data; typical storage, computation, and communication resources are required. Hence, this paper aims at introducing a Fog computing mechanism that takes into consideration the requitrements of special mobility, low latency and location awareness. Our solution based on controlling the movement of mobile cameras mounted on a fleet of mobile bees. A bee is an autonomous camera-drone, which gives the audience the feeling of being a part of such sports competition. It is a special type of Unmanned Aerial Vehicle (UAV) equipped with a built-in processing unit, memory, high-speed camera and transceiver. The bee has the ability to film the movement of the ball, which has to be followed and filmed via object tracking principle. In fact, detecting and tracking the ball from the broadcast soccer video constitute a major challenge. In soccer matches, the ball moves most of the time and it is frequently occluded while its size and shape appearance vary over the time and between cameras. Moreover, the feature-based tracking methods are used to judge whether or not a sole object is the target as the features of the ball might be changed fast over frames and then we cannot manage to distinguish the ball from other objects by using these methods. Thus, the current study demonstrates an innovative technique for tracking a soccer ball from mobile cameras fixed on multiple ball bees.

Research on Object Tracking Based on Graph Model in Sports Video

This article is about object tracking based on graph modeling. Object tracking is usually initialized by object detection methods. The fundamental hypothesis is that the object's pattern can be separated from its surrounding background sufficiently. However, for some objects, e.g., the ball in broadcast soccer videos, it is hard to extract effective features to detect the ball in a single video frame. The strategy adopted here is to identify the object's candidate regions in several consecutive frames, and then use a graph to construct the relationship between candidate regions. Finally, a Viterbi algorithm is used to extract the optimal path of the graph as the object's trajectory. This process is called short-term tracking. Then, it is used to initialize a Kalman filter to perform long-term tracking. In the process of tracking, the tracked region is verified to determine whether tracking is a failure, and short-term tracking is restarted if a failure happens.

Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

Robust player detection and tracking in broadcast soccer video based on enhanced particle filter

It is significant to detect and track soccer players in broadcast sports video, which is helpful to analysis player activity and team tactics. However, it is challenging to efficiently detect and track soccer players with shots switched and noise caused by auditorium and billboards. And for multi-player tracking how to treat the increase or decrease of player are also difficult. In this paper, a robust player detection algorithm based on salient region detection and tracking based on enhanced particle filtering are proposed. Salient region detection is used to segment sports fields, and then soccer players are detected by edge detection combined with Otsu algorithm. For soccer players tracking, we use an enhanced particle filter which we improve the algorithm in sample and the likelihood function combing the color feature and edge feature. Experimental results show the proposed algorithm can quickly and accurately detect and track soccer players in broadcast video.

Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model

Automated analysis of broadcast soccer game video is a challenging computer vision problem. Prior to performing high-level analysis (such as event detection), accurate classification of shot views and play–break segmentation are required to analyze the structure of soccer video. A novel deep network called parallel feature fusion network (PFF-Net) combines local and full-scene features to produce accurate shot view classification based on camera zoom and out-of-field status. Then, a novel hidden-to-observable Markov model (H2O-MM) is introduced to determine play/break status of the shots. Testing is performed using a variety of professional broadcast soccer videos. Variations of the PFF-Net are considered and compared with four existing methods where the PFF-Net demonstrates superior performance (92.6%). The H2O-MM has the accuracy of 98.7% for play–break segmentation, which is an improvement over two existing hidden Markov models. The new methods provide improved temporal labeling of broadcast soccer videos, which can be used to further improve overall automated event detection.

Detecting Soccer Goal Scenes from Broadcast Video using Telop Region

We propose a simple method to detect goal scenes from broadcast soccer video by using scoring telop regions. Scoring telop regions show us scores of both teams simultaneously with games. In the first step of our proposed method, we extract the telop regions which include scores of both teams. As next step, we try to recognize the score changes on the telop regions by using inter-frame difference between consecutive two frames. Then finally we can detect goal scenes as the timing of score changings. Experimental results show the ability of our proposed method. Keywords: Soccer video analysis, telop, goal scene detection, inter-frame difference.

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.

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.

Paper] Active Grid-based Pass Region Estimation from Multiple Frames of Broadcast Soccer Videos

An Active grid-based method for estimating pass regions from broadcast soccer videos is presented in this paper. It is assumed that the pass region has a high probability of the pass succeeding. In soccer matches, players discover pass regions based on previous and current player positions. In conventional methods, pass regions are estimated by applying Active Net to only a single frame of a soccer video. In the proposed method, Active grid is applied to three-dimensional data by which frames of the soccer video are connected with the temporal dimension. The proposed method then realizes robust estimation of pass regions based on multiple frames of player positions. The proposed method was applied to actual TV programs to verify its effectiveness.

Video Clips Classification with Hierarchical Approach

Clips classification, Hierarchical tree, low-level descriptors. Video sections Abstract. In this paper, we propose an approach with hierarchical tree for clips classification in broadcast soccer video. In this method, video frames are firstly classified and grouped into sections, and the sections are divided into clips. Then we can use the low-level descriptors to classify the clips. Low-level descriptions include color, motion descriptor and edge descriptor. Experiments on real soccer video programs show the encouraging results.

Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.

An Effective Slow-Motion Detection Approach for Compressed Soccer Videos

Slow-motion replays are content full segments of broadcast soccer videos. In this paper, we propose an efficient method for detection of slow-motion shots produced by high-speed cameras in soccer broadcasts. A rich set of color, motion, and cinematic features are extracted from compressed video by partial decoding of the MPEG-1 bitstream. Then, slow-motion shots are modeled by SVM classifiers for each shot class. A set of six full-match soccer games is used for training and evaluation of the proposed method. Our algorithm presents satisfactory results along with high speed for slow-motion detection in soccer videos.

Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

In this article, we present a method to perform automatic player trajectories mapping based on player detection, unsupervised labeling, efficient multi-object tracking, and playfield registration in broadcast soccer videos. Player detector determines the players' positions and scales by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We first learn the dominant color with accumulate color histogram at the beginning of processing, then use the player detector to collect hundreds of player samples, and learn player appearance codebook by unsupervised clustering. In a soccer game, a player can be labeled as one of four categories: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manual initialization. With the dominant color and player appearance model, we can locate and label each player. After that, we perform multi-object tracking by using Markov Chain Monte Carlo (MCMC) data association to generate player trajectories. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label consistency, motion consistency, and track length, etc. Finally, we extract key-points and find the mapping from an image plane to the standard field model, and then map players' position and trajectories to the field. A large quantity of experimental results on FIFA World Cup 2006 videos demonstrate that this method can reach high detection and labeling precision, reliably tracking in scenes of player occlusion, moderate camera motion and pose variation, and yield promising field registration results.

Content-aware broadcast soccer video retargeting using fuzzy logic

A content-aware video retargeting method is proposed for playing broadcast soccer video in small displays. Four visual perception clues are predefined based on soccer game-specific knowledge and modelled by visual attention features firstly. Then, a fuzzy logic inference system is proposed to estimate visual attention values (AVs) of ball and players by fusing attention features. AVs are later used to determine the region of interest (ROI) of each frame. Finally, a retargeted video is generated by the ROI of each frame with polynomial curve fitting for temporal smoothing. Both subjective and objective evaluation results are promising.

A Low Cost Algorithm for Expected Goal Events Detection in Broadcast Soccer Video

Recently, mining information in sports video data, especially soccer video, has become an active research topic. In this paper, a new algorithm for detection of expected goal events in soccer video is proposed. The proposed algorithm is composed of two main steps. Firstly, video is segmented into its constituent shots and these shots are categorized into two groups, namely long shots and non-long shots. Secondly, long shots are examined to detect expected goal events. Our scheme uses the playfield boundary feature for shot boundary detection, shot classification, and expected goal events detection. Therefore, this feature is extracted only one time and the algorithm is speedy. It is noticeable that the proposed algorithm is robust to blurring and spatial downsampling. Moreover, experimental results on various broadcast soccer videos show that our algorithm can achieve high accuracy and especially high speed.

Non-goal scene analysis for soccer video

The broadcast soccer video is usually recorded by one main camera, which is constantly gazing somewhere of playfield where a highlight event is happening. So the camera parameters and their variety have close relationship with semantic information of soccer video, and much interest has been caught in camera calibration for soccer video. The previous calibration methods either deal with goal scene, or have strict calibration conditions and high complexity. So, it does not properly handle the non-goal scene such as midfield or center-forward scene. In this paper, based on a new soccer field model, a field symbol extraction algorithm is proposed to extract the calibration information. Then a two-stage calibration approach is developed which can calibrate camera not only for goal scene but also for non-goal scene. The preliminary experimental results demonstrate its robustness and accuracy.

ROI-centered compression by adaptive quantization for sports video

In this paper, we propose a region of interest (ROI)-centered compression technique by adaptive quantization for sports videos. The loss of visual detail in an ROI in a broadcast soccer video is solved as seen in DMB (Digital Multimedia Broadcasting) through the mobile device. For tracking an ROI, in a soccer video, we track ball and player candidates using the Dynamic Kalman Filter (DKF) and send information of ROI macro-blocks (MBs) determined by tracking results to an encoder. The encoder allocates quantization step size (Qp) to ROI and non-ROI MBs based on priority and controls bit-rate using proposed rate-control (RC) algorithm. Experiment results show the proposed method increased visual detail in the ROI while maintaining a similar bit rate with the uniform quantization method.

A NOVEL ALGORITHM FOR EFFECTIVE BALL TRACKING

A novel method is proposed to achieve robust and real-time ball tracking in broadcast soccer videos. In sports video, the soccer ball is small, often occluded, and with high motion speed. Thus, it is difficult to detect the sole ball in a single frame. To solve this problem, rather than locate the ball in one of several frames through detection or tracking, we find the ball through optimizing its motion trajectory in successive frames. The proposed method includes three level processes: object level, intra-trajectory level, and inter-trajectory level processing. In object level, multiple objects instead of a single ball are detected and all of them are taken as ball candidates through shape and color features identification. Then at intra-trajectory level, each ball candidate is tracked by a Kalman filter and verified by detection in successive frames, which results in lots of initial short trajectories in a video shot. These trajectories are thereafter scored and filtered according to their length and spatial-temporal relationship in a time-line model. With these trajectories, we construct a distance graph, in which a node represents a trajectory, and an edge means distance between two trajectories. We then get the optimal path using the Dijkstra algorithm in the graph at the inter-trajectory level. The optimal path is composed by a sequence of initial trajectories which make the whole route smooth and long in duration. To get a complete and reasonable path, we finally apply cubic spline interpolation to bridge the gap between adjacent trajectories (the duration corresponding to when the ball is occluded). We select three representative real FIFA2006 soccer video clips (containing a total of 16,500 frames) and manually elaborately labeling each frame in it, and take it as ground-truth to evaluate the algorithm. The average F-score is 80.59%. The algorithm was used in our soccer analysis system and tested on a wide range of real soccer videos, and all the results are satisfied. The algorithm is effective and its whole speed far exceeds real-time, 35.6 fps on mpeg2 data on the Intel Conroe platform.