Martial Arts, Dancing and Sports dataset A challenging stereo and multi-view dataset for 3D human pose estimation.pdfVIP

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Martial Arts, Dancing and Sports dataset A challenging stereo and multi-view dataset for 3D human pose estimation.pdf

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Image and Vision Computing 61 (2017) 22–39 Contents lists available at ScienceDirect Image and Vision Computing journal homepage: /locate/imavis Martial Arts, Dancing and Sports dataset: A challenging stereo and multi-view dataset for 3D human pose estimation? Weichen Zhang*, Zhiguang Liu, Liuyang Zhou, Howard Leung, Antoni B. Chan Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region ARTICLE INFO Article history: Received 27 September 2015 Received in revised form 13 August 2016 Accepted 11 February 2017 Available online 21 February 2017 Keywords: Human pose estimation Robust tracking Evaluation Martial arts Dancing and sports ABSTRACT Human pose estimation is one of the most popular research topics in the past two decades, especially with the introduction of human pose datasets for benchmark evaluation. These datasets usually capture simple daily life actions. Here, we introduce a new dataset, the Martial Arts, Dancing and Sports (MADS), which consists of challenging martial arts actions (Tai-chi and Karate), dancing actions (hip-hop and jazz), and sports actions (basketball, volleyball, football, rugby, tennis and badminton). Two martial art masters, two dancers and an athlete performed these actions while being recorded with either multiple cameras or a stereo depth camera. In the multi-view or single-view setting, we provide three color views for 2D image-based human pose estimation algorithms. For depth-based human pose estimation, we provide stereo-based depth images from a single view. All videos have corresponding synchronized and calibrated ground-truth poses, which were captured using a Motion Capture system. We provide initial baseline results on our dataset using a variety of tracking frameworks, including a generative tracker based on the annealing particle ?lter and robust likelihood function, a discriminative tracker using twin Gaussian processes [1], and hybrid trackers, such