A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

Abstract-In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

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“…7. Tracking results on the soccer ISSIA dataset [53]. Note that there is little overlap between cameras on the same side of the field.…”

Section: Soccer -Issiamentioning

confidence: 99%

“…We use the publicly available ISSIA dataset [53]. It is made of 3,000 frames acquired by six cameras at a soccer match.…”

Section: Soccer -Issiamentioning

confidence: 99%

Multi-Commodity Network Flow for Tracking Multiple People

Shitrit

Berclaz

Fleuret

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

155

160

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“…This is followed by a connected component analysis that allows creating individual blobs and bounding boxes are created. An example output on an image of the SOCCER dataset [2] is shown in figure 1. This dataset holds sequences of six static cameras of a soccer game.…”

Section: Object Detectionmentioning

confidence: 99%

Multi-Camera Analysis of Soccer Sequences

Poppe

Bruyne

Verstockt

et al. 2010

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance

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The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset.

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“…The ISSIA dataset comprises a multi-view soccer sequence [33] of 3,000 frames captured by six full-HD 25-frames-per-second cameras placed in two sides of the soc- cer field. We use all the cameras for our experiments.…”

Section: Datasetsmentioning

confidence: 99%

Take your eyes off the ball: Improving ball-tracking by focusing on team play

Wang

Ablavsky

Shitrit

et al. 2014

Computer Vision and Image Understanding

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Abstract. Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, M is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences.

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A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

Cited by 98 publications

References 3 publications

Multi-Commodity Network Flow for Tracking Multiple People

Multi-Commodity Network Flow for Tracking Multiple People

Multi-Camera Analysis of Soccer Sequences

Take your eyes off the ball: Improving ball-tracking by focusing on team play

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