Morphological segmentation of sport scenes using color information

Naemura, Masahide; Fukuda, Akira; Mizutani, Yasuhiro; Izumi, Yasuo; Tanaka, Yutaka; Enami, Kazumasa

doi:10.1109/11.892154

Cited by 32 publications

(14 citation statements)

References 14 publications

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“…The detection step using color-based techniques is usually fast, but it is very sensitive to illumination variations, which may reduce their robustness and precision. Moreover, the techniques in [38,31] demand on a laborious training step.…”

Section: Non-intrusive Systemsmentioning

confidence: 99%

“…Some works are based on fixed cameras [30,10,11,24,18,20,32,17,46,38], since they can capture, in most cases, all players actions in the game region, while moving cameras or broadcast images, on the other hand, can not always view all players in the scene during the entire game, which causes the loss of certain actions. Nevertheless, many works in that field use these types of image sources [22,35,49,15,23,31], as they are most likely easier to obtain. For the detection step, different techniques can be applied.…”

Section: Non-intrusive Systemsmentioning

confidence: 99%

“…In [13,22,17,15,46], a model that represent the game region is built based on color information, that is, the predominant game region color is used to create a color model of the background, allowing the extraction of regions that contain players. Similarly, the authors in [38,31] use histograms and color distributions to build a model that represents the player and it is used on the segmentation process. This model is built from players samples manually collected in a training phase.…”

Section: Non-intrusive Systemsmentioning

confidence: 99%

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A vision-based system to support tactical and physical analyses in futsal

Padua

Pádua

Pereira

et al. 2017

Machine Vision and Applications

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This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed by costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players' occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential.

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Section: Non-intrusive Systemsmentioning

confidence: 99%

Section: Non-intrusive Systemsmentioning

confidence: 99%

Section: Non-intrusive Systemsmentioning

confidence: 99%

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A vision-based system to support tactical and physical analyses in futsal

Padua

Pádua

Pereira

et al. 2017

Machine Vision and Applications

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“…Chromakey technology, which can extract object shapes very precisely, achieves its robustness and precision by manipulating the background artificially. In order to obtain accurate outline shapes even for backgrounds of natural images while using background color information, methods that use morphological processing and region growth [3] have been proposed. The background subtraction method which can be used even when the background is not uniform can be used even when the camera is fixed in position, and so is oriented toward crime prevention and traffic monitoring applications in which the computational load is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Scene‐adaptive switching of segmentation methods for object‐based video encoding

Misu¹,

Sonehara

Zheng³

et al. 2004

Systems & Computers in Japan

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SUMMARYIn order to create efficient object-based video encoding, the authors have proposed a scene-adaptive region segmentation method that can work with a variety of different types of scenes. In their method, encoding efficiency is improved by adaptively selecting for each frame the optimal method from among several different region segmentation methods determined using Bayesian decision-making based on the video features of a scene. In this paper, the authors describe the Bayesian decision and training methods, and then formalize the image features and the different region segmentation methods. Finally, the authors demonstrate the validity of the proposed approach through experiments.

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“…The above-mentioned two categories of algorithms are based on different definitions and are different approaches. Generally speaking [20], algorithms by immersion are more computationally efficient than algorithms by topographical distance, and are therefore more widely used [6,7,12,[15][16][17][18]20,25,26].…”

Section: Introductionmentioning

confidence: 99%