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Human body pose detection using Bayesian spatio-temporal templates

Human body pose detection using Bayesian spatio-temporal templates,10.1016/j.cviu.2006.07.007,Computer Vision and Image Understanding,Miodrag Dimitrij

Human body pose detection using Bayesian spatio-temporal templates   (Citations: 25)
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We present a template-based approach to detecting human silhouettes in a specific walking pose. Our templates consist of short sequences of 2D silhouettes obtained from motion capture data. This lets us incorporate motion information into them and helps dis- tinguish actual people who move in a predictable way from static objects whose outlines roughly resemble those of humans. Moreover, during the training phase we use statistical learning techniques to estimate and store the relevance of the different silhouette parts to the recognition task. At run-time, we use it to convert Chamfer distance to meaningful probability estimates. The templates can handle six different camera views, excluding the frontal and back view, as well as different scales. We demonstrate the effectiveness of our technique using both indoor and outdoor sequences of people walking in front of cluttered backgrounds and acquired with a moving camera, which makes techniques such as background subtraction impractical.
Journal: Computer Vision and Image Understanding - CVIU , vol. 104, no. 2-3, pp. 127-139, 2006
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    • ...Non-edge-based likelihood measures include optical flow [11, 73], flow occlusion/disocclusion b oundaries [79], segmented silhouettes based on level sets [65], image templates [89], spatio-temporal templates [18], principal component-based models of appearance [72], and robust on-line local [6, 85] and global appearance models [6]...

    Leonid Sigalet al. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline A...

    • ...We therefore use a chamfer-based method [11] that was designed to detect key postures from any viewpoint, even when the background is cluttered and background subtraction is impractical because the camera moves, as is the case in Fig. 1a. Because the detected postures are projections of 3D models, we can map them back to full 3D poses and use them to select and warp motions from a training database that closely match them...
    • ...We rely on spatiotemporal templates to detect the people in canonical poses [11] and on PCA-based motion models [44] to perform the interpolation...
    • ...We first use a template-based approach [11] to detect people in poses that are most characteristic of the target activity, as shown in Fig. 1a. The templates consist of consecutive 2D silhouettes obtained from 3D motion capture data seen from six different camera views and at different scales...
    • ...3.2 Detection and Initialization As in our earlier publication [11], people in canonical poses are detected using spatiotemporal templates that are sequences of three silhouettes of a person, such as the one of Fig. 2c. The first corresponds to the moment just before they reach the target pose, the second to the moment when they have precisely the right attitude, and the third just after...
    • ...We refer the interested reader to our earlier publication for further details [11]...
    • ...This makes sense because the coefficients used to weight the contributions are designed to account for the relevance of different silhouette portions in a Bayesian framework [11]...

    Andrea Fossatiet al. From Canonical Poses to 3D Motion Capture Using a Single Camera

    • ...Some posture recognition algorithms that do not need foreground extraction or pose estimation exist. They relied on chamfer distance [12,13]...

    Keechul Jung. Human Pose Recognition Using Chamfer Distance in Reduced Background Ed...

    • ...texture-based approaches for detection would fail, whereas on the other hand, classical template-based matching algorithms [19, 8, 28], which use a priori edge information, can deal with these limitations...
    • ...Object detectors, which are efficient and work in real-time are specialized and designed for specific objects such as faces [33, 23, 5], pedestrians [8, 14, 6] etc...

    Stefan Holzeret al. Distance Transform Templates for Object Detection and Pose Estimation

    • ...Such methods which are based on extracting positions of the body joints as the user movements are registered between video frames have been reported extensively in the literature [4,16,17,19,20]...

    Yasmin Aghajanet al. Home Exercise in a Social Context: Real-Time Experience Sharing Using ...

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