Robust tracking of persons in real-world scenarios using a statistical computer vision approach

Rigoll, Gerhard; Breit, H.; Wallhoff, Frank

doi:10.1016/j.imavis.2003.09.014

Cited by 7 publications

(3 citation statements)

References 24 publications

(20 reference statements)

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“…Besides sport video analysis, there are many other tracking applications including video surveillance [16], smart environments [17, 18], face tracking [19, 20], human pose estimation [21] etc. Pedestrian tracking [22, 23] is well‐studied, and is similar to player tracking in soccer. However, because of the assumption of an upright shape and a smooth motion, it is not suitable to track highly deformable players both in size and shape.…”

Section: Introductionmentioning

confidence: 99%

Multiplayer tracking system for short track speed skating

Wang

Cheng

Shan

2014

IET Computer Vision

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In this study, the authors propose a novel and effective short track speed skating tracking system. Aimed at several challenging tracking problems of short track skating: long-time occlusion and complex group situations, size variations, similar or identical uniforms, fast motion speed, quick orientation changes etc.; a novel fuzzy model is proposed to initialise contours of skaters even they wear the same kind of uniform. Then, a novel approach for multiple object tracking is developed to track skaters reliably. The experimental results demonstrate that the proposed system can solve the above challenging problems very effectively. The information provided by the proposed system, including trajectories, velocity analysis and two-dimensional reconstruction animations, is valuable to broadcasters, athletes and coaches alike. The proposed tracking algorithm can obtain better results than that by the other eight state-of-the-art trackers in short track speed skating tracking.

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Section: Introductionmentioning

confidence: 99%

Multiplayer tracking system for short track speed skating

Wang

Cheng

Shan

2014

IET Computer Vision

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“…Haritaoglu et al (Haritaoglu et al, 2000) use a variation of the background to detect object in the foreground. Rigoll et al (Rigoll et al, 2004) use stochastic modeling techniques, Pseudo-2D Hidden Markov Models (P2DHMMS) and Kalman filter to estimate the location of a person. In (Ozyildiz et al, 2003;Rasmussen & Hager, 2001;Yilmaz et al, 2004), a technique of fusing multiple cues (e.g.…”

Section: Introductionmentioning

confidence: 99%

Human Motion Tracking Based on Unscented Kalman Filter in Sports Domain

Liu¹,

Tang²

2010

Kalman Filter

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“…Haritaoglu et al [5] use a variation of the background to detect object in the foreground. Rigoll et al [11] use stochastic modeling techniques, Pseudo-2D Hidden Markov Models (P2DHMMS) and Kalman filter to estimate the location of a person. In [12][13][14], a technique of fusing multiple cues (e.g.…”

Section: Introductionmentioning

confidence: 99%