Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition? Fashion gurus say they can! In our work, we study this question from the perspective of computer vision. In particular, we want to understand whether existing computer vision approaches can be used to automatically extract the qualities exhibited by the Miss Universe winners during their catwalk. This study can pave the way towards new vision-based applications for the fashion industry. To this end, we propose a novel video dataset, called the Miss Universe dataset, comprising 10 years of the evening gown competition selected between 1996–2010. We further propose two ranking-related problems: (1) Miss Universe Listwise Ranking and (2) Miss Universe Pairwise Ranking. In addition, we also develop an approach that simultaneously addresses the two proposed problems. To describe the videos we employ the recently proposed Stacked Fisher Vectors in conjunction with robust local spatio-temporal features. From our evaluation we found that although the addressed problems are extremely challenging, the proposed system is able to rank the winner in the top 3 best predicted scores for 5 out of 10 Miss Universe competitions.
Gait and walk assessments have been investigated for elderly people and humans with neurological disorders @cite_7 @cite_17 . Two web-cams are used to extract gait parameters including walking speed, step time, and step length in @cite_7 . The gait parameters are used for a fall risk assessment tool for home monitoring of older adults. For rehabilitation and treatment of patients with neurological disorders, automatic gait analysis with a Microsoft Kinect sensor is used to quantify the gait abnormality of patients with multiple sclerosis @cite_17 . A gait analysis system consisting of two camcoders located on the right and left side of a treadmill is employed in @cite_18 . This system fully reconstructs the skeleton model and demonstrates good accuracy compared to Kinect sensors. Despite being a related problem, for our Miss Universe catwalk analysis, Kinect sensors or multi-cameras are simply not available. The assessment of quality of actions using only visual information is still under early development. A recent work to predict the expert judges' scores for actions diving and figure skating in the Olympic games is presented in @cite_33 . The concept behind the score prediction is to learn how to assess the quality of actions in videos.
@cite_18 @cite_33 @cite_7 @cite_17
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Gait analysis is a domain of interest in clinical medical practice, both for neurological and non-neurological abnormal troubles. Marker-based systems are the most favored methods of human motion assessment and gait analysis, however, these systems require specific equipment and expertise and are cumbersome, costly and difficult to use. In this paper we compare two low-cost and marker-less systems that are: (1) A Kinect in front of a treadmill and (2) a set of two camcorders on the sides of the treadmill, used to reconstruct the skeleton of a subject during walk. We validated our method with ground truth data obtained with markers manually placed on the subject’s body. Finally, we present an application for asymmetric gait recognition. Our results on different subjects showed that, compared to the Kinect, the two-camcorder approach was very efficient and provided accurate measurements for gait assessment. While recent advances in computer vision have provided reliable methods to recognize actions in both images and videos, the problem of assessing how well people perform actions has been largely unexplored in computer vision. Since methods for assessing action quality have many real-world applications in healthcare, sports, and video retrieval, we believe the computer vision community should begin to tackle this challenging problem. To spur progress, we introduce a learning-based framework that takes steps towards assessing how well people perform actions in videos. Our approach works by training a regression model from spatiotemporal pose features to scores obtained from expert judges. Moreover, our approach can provide interpretable feedback on how people can improve their action. We evaluate our method on a new Olympic sports dataset, and our experiments suggest our framework is able to rank the athletes more accurately than a non-expert human. While promising, our method is still a long way to rivaling the performance of expert judges, indicating that there is significant opportunity in computer vision research to improve on this difficult yet important task. In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0.99 and repeatability coefficients between 0.7 and 6.6 was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21 in walking speed, a mean decrease of 12 in step time, and a mean increase of 6 in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.