Depth consistency evaluation for error-pose detection

Sou-Young Jin; Ho-Jin Choi; Youssef Iraqi

doi:10.1117/12.2051580

24 December 2013 Depth consistency evaluation for error-pose detection

Sou-Young Jin, Ho-Jin Choi, Youssef Iraqi

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671F (2013) https://doi.org/10.1117/12.2051580
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

With the development of depth sensors, i.e. Kinect, it is now possible to predict human body poses from a depthmap without any manual labeling. The predicted poses can be used as meaningful features for many applications such as human action recognition. However, existing pose estimation algorithms are not perfect, which can seriously affect the performance of its following applications. In this paper, we propose a novel method to detect erroneous poses. Human poses are captured by Kinect SDK which predicts body joints and connects them with straight lines to represent a pose. We observe depth gradient of pixels located on a body part is consistent when the body part is predicted correctly. With this observation, our algorithm examines depth gradients of pixels on each body part. During the depth gradient processing, our algorithm also considers occlusions. Once a sudden change is detected in depth values on a body part, we check whether the gradient is still consistent excluding the sudden change region. We tested our algorithm on many human activities and our experimental results show that our algorithm acceptably detects erroneous poses in real time.

Citation Download Citation

Sou-Young Jin, Ho-Jin Choi, and Youssef Iraqi "Depth consistency evaluation for error-pose detection", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671F (24 December 2013); https://doi.org/10.1117/12.2051580

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KEYWORDS

Detection and tracking algorithms

Sensors

Error analysis

Image analysis

RGB color model

Image sensors

Machine vision

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