Occupant Classification Using Range Images

Devarakota, Pandu; Castillo-Franco, M.; Ginhoux, R.; Mirbach, Bruno; Ottersten, Björn

doi:10.1109/tvt.2007.897645

Cited by 20 publications

(22 citation statements)

References 18 publications

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“…ToF sensors provide a direct way for acquiring 3D surface information of objects with a single allsolid-state ToF camera by measuring the time of flight of an actively emitted optical reference signal in the infrared spectral range [2]. More recently, applications like obstacle detection [12], gesture recognition [5][6] and automotive passenger classification [4] We investigated the possibilities of extracting a respiration signal from these 3D data and to distinguish abdominal and thoracic breathing based on this information. To emphasize technical differences between markerbased/marker-less stereo-vision approaches, spirometry and our ToF-based approach we briefly will compare certain techniques in the following: Marker-less stereo vision vs. ToF: The main difference between both systems is how depth information is achieved.…”

Section: State Of the Artmentioning

confidence: 99%

Robust real-time 3D respiratory motion detection using time-of-flight cameras

et al. 2008

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Section: State Of the Artmentioning

confidence: 99%

Robust real-time 3D respiratory motion detection using time-of-flight cameras

et al. 2008

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“…For example, for Smart Airbag systems, Devarakota et al [6] used 3-D images to classify vehicle occupants as adults or children, leaning forward or backward. A pedestrian detection system based on depth and intensity images was proposed by Rapus et al [7].…”

Section: Related Workmentioning

confidence: 99%

Scene Segmentation and Pedestrian Classification from 3-D Range and Intensity Images

Xue

Phung

Bouzerdoum

2012

2012 IEEE International Conference on Multimedia and Expo

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. (2012). Scene segmentation and pedestrian classification from 3-D range and intensity images. IEEE International Conference on . Australia: IEEE. Scene segmentation and pedestrian classification from 3-D range and intensity images AbstractThis paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian obstacles. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 99.5%. ABSTRACTThis paper proposes a new approach to classify obstacles using a time-of-flight camera, for applications in assistive navigation of the visually impaired. Combining range and intensity images enables fast and accurate object segmentation, and provides useful navigation cues such as distances to the nearby obstacles and obstacle types. In the proposed approach, a 3-D range image is first segmented using histogram thresholding and mean-shift grouping. Then Fourier and GIST descriptors are applied on each segmented object to extract shape and texture features. Finally, support vector machines are used to recognize the obstacles. This paper focuses on classifying pedestrian and non-pedestrian obstacles. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 99.5%.

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“…4 The given values refer to the error in reconstructing a 3D geometry of a cube with known dimensions from multiple acquired frames. 5 The given values refer to the relative error in estimating the translation/rotation of the robot the data acquisition devices were mounted on.…”

Section: State Of the Artmentioning

confidence: 99%

“…Time-of-Flight (ToF) imaging provides a direct way for acquiring 3D surface information of objects and scenes in the current field-of-view [1]. More recently, ToF sensors are used in a wider range of applications like obstacle detection [2], gesture recognition [3] [4] and automotive passenger classification [5]. For more complex application areas like map building, robot navigation or scene exploration, building a 3D model of a scene larger than the field-of-view of the applied ToF sensor imposes a 3D/3D-registration problem: Partially nonoverlapping 3D surface points have to be transformed into a common coordinate system by estimating the extrinsic parameters of the range measuring sensor at each acquisition time step.…”

mentioning

confidence: 99%

Robust real-time 3D modeling of static scenes using solely a Time-of-Flight sensor

Feulner

Penne

Kollorz

et al. 2009

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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Abstract-An algorithm is proposed for the 3D modeling of static scenes solely based on the range and intensity data acquired by a Time-of-Flight camera during an arbitrary movement. No additional scene acquisition devices, like inertia sensor, positioning robots or intensity based cameras are incorporated. The current pose is estimated by maximizing the uncentered correlation coefficient between edges detected in the current and a preceding frame at a minimum frame rate of four fps and an average accuracy of 45 mm. The paper also describes several extensions for robust registration like multiresolution hierarchies and projection Iterative Closest Point algorithm. The basic registration algorithm and its extensions were intensively evaluated against ground truth data to validate the accuracy, robustness and real-time-capability.

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Occupant Classification Using Range Images

Cited by 20 publications

References 18 publications

Robust real-time 3D respiratory motion detection using time-of-flight cameras

Robust real-time 3D respiratory motion detection using time-of-flight cameras

Scene Segmentation and Pedestrian Classification from 3-D Range and Intensity Images

Robust real-time 3D modeling of static scenes using solely a Time-of-Flight sensor

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