Detection of swimmer using dense optical flow motion map and intensity information

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

9 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)75-101
Journal / PublicationMachine Vision and Applications
Volume24
Issue number1
Publication statusPublished - Jan 2013

Abstract

A vision-based system that can locate individual swimmers and recognize the activities is applicable for swimming gait analysis, drowning event detection, etc. The system relies on accurate detection of swimmer's body parts such as head and upper limbs. The swimmer detection problem can be regarded as background subtraction. Swimmer detection in the aquatic environment is very difficult due to a dynamic background with water ripples, splashes, specular reflections, etc. This paper presents a swimmer detection method which utilizes both local motion and intensity information estimated from the image sequence. Local motion information is obtained by computing dense optical flow and periodogram. We adopt a heuristic approach to generate a motion map characterizing the local motions (random/stationary, ripple or swimming) of image pixels over a short duration. Intensity information is modeled as a mixture of Gaussians. Finally, using the motion map and the Gaussian models, swimmers are detected in each video frame. We test the method on video sequences captured at daytime, and nighttime, and of different swimming styles (breaststroke, freestyle, backstroke). Our method can detect swimmers much better than that using intensity information alone. In addition, we compare our method with existing algorithms - codebook model and self-organizing artificial neural networks. The methods are tested on publicly available video sequence and our swimming video sequence. We show through the quantitative measures the superiority of our method. © 2012 Springer-Verlag.

Research Area(s)

  • Aquatic environment, Background subtraction, Dense optical flow, Swimming, Video surveillance