Who's Who in a Sports Video? An Individual Level Sports Video Indexing System

Sun, Shih-Wei; Cheng, Winton; Hung, Y. Y.; Fan, Ivy; Liu, Chris; Hung, Jacqueline; Lin, Chia-Kai; Liao, Hong-Yuan Mark

doi:10.1109/icme.2012.59

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…To validate the proposed 2D histogram-based player localization, we compare our approach to (1) connected component analysis (CCA)-based approach, which is a traditional object segmentation method widely used in most applications of automatic player localization [14,15], and (2) a supervised learning approach [11] adopting histogram of oriented gradient (HOG) features. A dominant color-based approach is also performed to retain the player masks for CCA.…”

Section: D Histogram-based Player Localizationmentioning

confidence: 99%

“…Morais et al [10] introduce an automatic system for estimating the positions of futsal players by Haarlike features and particle filters with multiple cameras. Sun et al [11] automatically recognize each player by HOG features in a multi-player game to achieve the individual level indexing. Lu et al [12] detect multiple players by DPM features and identify them by weak visual cues and both temporal and mutual exclusion constraints in a conditional random field.…”

Section: Related Workmentioning

confidence: 99%

“…Supervised learning methods constructing detectors using features like Haar [6], histogram of oriented gradient (HOG) [7,8], and deformable part model (DPM) [9] are employed in the majority of existing works [10][11][12] (to cite a few). The detectors can achieve high accuracy but Abstract Player location is one of the most informative cues for obtaining tactics arrangement and collecting descriptive game statistics.…”

Section: Motivationmentioning

confidence: 99%

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2D Histogram-based player localization in broadcast volleyball videos

et al. 2015

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Section: D Histogram-based Player Localizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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2D Histogram-based player localization in broadcast volleyball videos

et al. 2015

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“…Sports video analysis has become popular and applied to many commercial applications [1][2][3][4][5][6][7][8]. With the help of modern object detection and tracking techniques [9][10][11][12][13][14], player tracking and identification from sport videos have received great attentions [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Identification and tracking of players in sport videos

Lin

Hsu

et al. 2013

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

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In this paper, we propose a novel framework to automatically perform player tracking and identification for sport videos filmed by a single pan-tilt-zoom camera from the court view. The proposed scheme is separated into three parts. The first part is to detect players by a deformable part model. The second part is to recognize jersey numbers by gradient differences and optical character recognition. The final part applies particle filters to track players. Experimental results demonstrate the efficacy of the proposed algorithm and the feasibility for sports video analysis.

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“…The system introduced by Lu et al [135] identified players in broadcast sports videos using conditional random fields and achieved a player recognition accuracy up to 85% on unlabeled NBA basketball clips. Sun et al [136] proposed an individual level sports video indexing scheme, where a principal axis-based contour descriptor is used is to solve the jersey number recognition problem. Lu et al [137] proposed a novel linear programming relaxation algorithm for predicting player identification in a video clip using weakly supervised learning with play-by-play texts, which greatly reduced the number of labelled training examples required.…”

Section: Person Tracking and Identificationmentioning

confidence: 99%

Human detection in surveillance videos and its applications - a review

Paul

Haque

Chakraborty

2013

EURASIP J. Adv. Signal Process.

148

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Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. Review IntroductionOver the recent years, detecting human beings in a video scene of a surveillance system is attracting more attention due to its wide range of applications in abnormal event detection, human gait characterization, person counting in a dense crowd, person identification, gender classification, fall detection for elderly people, etc.The scenes obtained from a surveillance video are usually with low resolution. Most of the scenes captured by a static camera are with minimal change of background. Objects in the outdoor surveillance are often detected in far field. Most existing digital video surveillance systems rely on human observers for detecting specific activities in a real-time video scene. However, there are limitations in the human capability to monitor simultaneous events in surveillance displays [1]. Hence, human motion analysis in automated video surveillance has become one of the most active and attractive research topics in the area of computer vision and pattern recognition.An intelligent system detects and captures motion information of moving targets for accurate object classification. The classified object is being tracked for high-level analysis. In this study, we focus on detecting humans and do not consider recognition of their complex activities. Human detection is a difficult task from a machine vision perspective as it is influenced by a wide range of possible appearance due to changing articulated pose, clothing, lighting and background, but prior knowledge on these limitations can improve the detection performance.The detection process generally occurs in two steps: object detection and object classification. Object detection could be performed by background subtraction, optical flow and spatio-temporal filtering. Background subtraction is a popular method for object detection where it attempts to detect moving objects from the difference between the current frame and a background frame in a pixelby-pixel or block-by-block fashion. There are few available approaches to perform background subtraction. The most common ones are adaptive Gaussian mixture [2-10], non-parametric background [11][12][13][1...

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Who's Who in a Sports Video? An Individual Level Sports Video Indexing System

Cited by 5 publications

References 12 publications

2D Histogram-based player localization in broadcast volleyball videos

2D Histogram-based player localization in broadcast volleyball videos

Identification and tracking of players in sport videos

Human detection in surveillance videos and its applications - a review

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