The Experts below are selected from a list of 1734 Experts worldwide ranked by ideXlab platform

Anton Kummert - One of the best experts on this subject based on the ideXlab platform.

  • Pedestrian detection using a single-Monochrome Camera.
    IET Intelligent Transport System. Mar2009, 2009
    Co-Authors: G. Ma, S. Müller-Schneiders, S B Park, D. Mueller, Anton Kummert
    Abstract:

    A car-mounted single Monochrome Camera-based pedestrian detection algorithm is discussed. The detection range is divided into two sub-regions, that is, the near distance range and the far distance range. Two different detection algorithms are applied in the two regions. For the near distance range, where the direction of the motion of the detected obstacle is important, a motion segmentation approach using interest points is utilised. For the far distance range where the motion of the detected obstacle is not as important, a robust and computationally efficient modified inverse perspective mapping-based obstacle detection is utilised. Finally, a low-level pedestrian-oriented segmentation algorithm, which is aimed at the depth information of the detected pedestrian candidate, is also presented. [ABSTRACT FROM AUTHOR]

  • Pedestrian detection using a singleMonochrome Camera
    IET Intelligent Transport Systems, 2009
    Co-Authors: D. Muller, S B Park, S. Muller-schneiders, Anton Kummert
    Abstract:

    A car-mounted single Monochrome Camera-based pedestrian detection algorithm is discussed. The detection range is divided into two sub-regions, that is, the near distance range and the far distance range. Two different detection algorithms are applied in the two regions. For the near distance range, where the direction of the motion of the detected obstacle is important, a motion segmentation approach using interest points is utilised. For the far distance range where the motion of the detected obstacle is not as important, a robust and computationally efficient modified inverse perspective mapping-based obstacle detection is utilised. Finally, a low-level pedestrian-oriented segmentation algorithm, which is aimed at the depth information of the detected pedestrian candidate, is also presented.

  • ITSC - 3D Traffic Sign Tracking Using a Particle Filter
    2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
    Co-Authors: Mirko Meuter, Anton Kummert, Stefan Müller-schneiders
    Abstract:

    In recent years, there was much activity in the development of Camera based active safety systems to aid and to support the driver of a car. One application for such a system is the detection and classification of traffic signs. An important aspect of such a system is the tracking of traffic signs. We present a novel algorithm to track traffic signs in 3D using a single Monochrome Camera. The algorithm allows to use the constraint that the observed movement on the image plane is entirely caused by the host car movement, which is partially known from internal sensors. The usage of the sensor information improves the tracking process and allows a robust rejection of false positive detections. We also present a way to incorporate a shape cue directly from the image plane into the tracking process. First tests show good results in practice and indicate, that this kind of tracking makes a very valuable addition to a traffic sign detection system.

  • A REAL-TIME DETECTION ALGORITHM FOR VISION-BASED PEDESTRIAN PROTECTION
    International Journal of Information Acquisition, 2008
    Co-Authors: Guanglin Ma, Su-birm Park, Alexander Ioffe, Stefan Müller-schneiders, Anton Kummert
    Abstract:

    This paper discusses the robust, real-time detection of stationary and moving pedestrians utilizing a single car-mounted Monochrome Camera. First, the system detects potential pedestrians above the ground plane by combining conventional Inverse Perspective Mapping (IPM)-based obstacle detection with the vertical 1D profile evaluation of the IPM detection result. Usage of the vertical profile increases the robustness of detection in low-contrast images as well as the detection of distant pedestrians significantly. A fast digital image stabilization algorithm is used to compensate for erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low-level pedestrian-oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented. A novel approach termed Pedestrian Detection Strip (PDS) is used to improve the calculation time by a factor of six compared to conventional approaches.

  • 3D Traffic Sign Tracking Using a Particle Filter
    2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
    Co-Authors: Mirko Meuter, Anton Kummert, Stefan Müller-schneiders
    Abstract:

    In recent years, there was much activity in the development of Camera based active safety systems to aid and to support the driver of a car. One application for such a system is the detection and classification of traffic signs. An important aspect of such a system is the tracking of traffic signs. We present a novel algorithm to track traffic signs in 3D using a single Monochrome Camera. The algorithm allows to use the constraint that the observed movement on the image plane is entirely caused by the host car movement, which is partially known from internal sensors. The usage of the sensor information improves the tracking process and allows a robust rejection of false positive detections. We also present a way to incorporate a shape cue directly from the image plane into the tracking process. First tests show good results in practice and indicate, that this kind of tracking makes a very valuable addition to a traffic sign detection system.

Su-birm Park - One of the best experts on this subject based on the ideXlab platform.

  • A REAL-TIME DETECTION ALGORITHM FOR VISION-BASED PEDESTRIAN PROTECTION
    International Journal of Information Acquisition, 2008
    Co-Authors: Guanglin Ma, Su-birm Park, Alexander Ioffe, Stefan Müller-schneiders, Anton Kummert
    Abstract:

    This paper discusses the robust, real-time detection of stationary and moving pedestrians utilizing a single car-mounted Monochrome Camera. First, the system detects potential pedestrians above the ground plane by combining conventional Inverse Perspective Mapping (IPM)-based obstacle detection with the vertical 1D profile evaluation of the IPM detection result. Usage of the vertical profile increases the robustness of detection in low-contrast images as well as the detection of distant pedestrians significantly. A fast digital image stabilization algorithm is used to compensate for erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low-level pedestrian-oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented. A novel approach termed Pedestrian Detection Strip (PDS) is used to improve the calculation time by a factor of six compared to conventional approaches.

  • A Real Time Object Detection Approach Applied to Reliable Pedestrian Detection
    2007 IEEE Intelligent Vehicles Symposium, 2007
    Co-Authors: Su-birm Park, Alexander Ioffe, Stefan Müller-schneiders, Anton Kummert
    Abstract:

    This paper presents a robust real time obstacle and pedestrian detection algorithm, which is capable of handling the challenges of stationary as well as moving objects, utilizing a single car mounted Monochrome Camera. First, the system detects obstacles above the ground plane by obtaining a "virtual stereo system" through the usage of inverse perspective mapping. A fast digital image stabilization algorithm is used to compensate erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low level pedestrian segmentation algorithm is developed to extract bounding boxes of potential pedestrians. Furthermore a novel approach called the pedestrian detection strip is used to improve the calculation time by a factor of six compared to previous attempts. Experiments have been carried out by applying the proposed algorithm on prerecorded sequences as well as within a test vehicle and thus in a closed loop environment. The experimental results indicate a promising detection performance. Obstacles and pedestrians up to 50 meters away from the vehicle have been detected reliably at 64 frames per second on a 3 GHz PC.

  • ITSC - Vision-Based Pedestrian Detection -- Improvement and Verification of Feature Extraction Methods and SVM-Based Classification
    2006 IEEE Intelligent Transportation Systems Conference, 2006
    Co-Authors: Sam Schauland, Anton Kummert, Su-birm Park, Uri Iurgel, Yan Zhang
    Abstract:

    Feature extraction and classification are two of the most important modules of any vision-based pedestrian detection system, since they are critical to the performance of the system as a whole. This paper presents the feature extraction and classification modules of a vision-based pedestrian detection system using a vehicle-mounted Monochrome Camera. The feature extraction module includes two kinds of features: wavelet-based features and a combination of simple symmetry and edge density features. Support vector machines based on a modified version of libSVM (Chang and Lin, 2001) are used for classification, and, for feature selection and optimization of feature space size, a fast and simple method using image masks for both feature types is presented. We have trained and tested our system using pedestrian and non-pedestrian images extracted from video sequences showing daylight urban traffic scenes

  • Vision-Based Pedestrian Detection -- Improvement and Verification of Feature Extraction Methods and SVM-Based Classification
    2006 IEEE Intelligent Transportation Systems Conference, 2006
    Co-Authors: Sam Schauland, A. Kummert, Su-birm Park, Uri Iurgel, Yan Zhang
    Abstract:

    Feature extraction and classification are two of the most important modules of any vision-based pedestrian detection system, since they are critical to the performance of the system as a whole. This paper presents the feature extraction and classification modules of a vision-based pedestrian detection system using a vehicle-mounted Monochrome Camera. The feature extraction module includes two kinds of features: wavelet-based features and a combination of simple symmetry and edge density features. Support vector machines based on a modified version of libSVM (Chang and Lin, 2001) are used for classification, and, for feature selection and optimization of feature space size, a fast and simple method using image masks for both feature types is presented. We have trained and tested our system using pedestrian and non-pedestrian images extracted from video sequences showing daylight urban traffic scenes

Jurek Z. Sasiadek - One of the best experts on this subject based on the ideXlab platform.

  • MMAR - Laboratory experimentation of stereo vision-based relative navigation with unknown spinning spacecraft
    2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), 2015
    Co-Authors: Setareh Yazdkhasti, Steve Ulrich, Jurek Z. Sasiadek
    Abstract:

    In this paper, a vision-based relative navigation strategy applicable for an unknown and spinning target object in space is presented. Specifically, the relative navigation system uses a calibrated stereo Monochrome Camera, and is developed for estimating the relative position, linear velocity and angular velocity between the inspector spacecraft and the unknown target object, while minimizing the computational requirements. Experimental results in a dark room with a scaled model of a spacecraft spinning about its major axis of rotation are reported to demonstrate the performance and applicability of the navigation system for small inspection spacecraft.

  • Laboratory experimentation of stereo vision-based relative navigation with unknown spinning spacecraft
    2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), 2015
    Co-Authors: Setareh Yazdkhasti, Steve Ulrich, Jurek Z. Sasiadek
    Abstract:

    In this paper, a vision-based relative navigation strategy applicable for an unknown and spinning target object in space is presented. Specifically, the relative navigation system uses a calibrated stereo Monochrome Camera, and is developed for estimating the relative position, linear velocity and angular velocity between the inspector spacecraft and the unknown target object, while minimizing the computational requirements. Experimental results in a dark room with a scaled model of a spacecraft spinning about its major axis of rotation are reported to demonstrate the performance and applicability of the navigation system for small inspection spacecraft.

Stefan Müller-schneiders - One of the best experts on this subject based on the ideXlab platform.

  • ITSC - 3D Traffic Sign Tracking Using a Particle Filter
    2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
    Co-Authors: Mirko Meuter, Anton Kummert, Stefan Müller-schneiders
    Abstract:

    In recent years, there was much activity in the development of Camera based active safety systems to aid and to support the driver of a car. One application for such a system is the detection and classification of traffic signs. An important aspect of such a system is the tracking of traffic signs. We present a novel algorithm to track traffic signs in 3D using a single Monochrome Camera. The algorithm allows to use the constraint that the observed movement on the image plane is entirely caused by the host car movement, which is partially known from internal sensors. The usage of the sensor information improves the tracking process and allows a robust rejection of false positive detections. We also present a way to incorporate a shape cue directly from the image plane into the tracking process. First tests show good results in practice and indicate, that this kind of tracking makes a very valuable addition to a traffic sign detection system.

  • A REAL-TIME DETECTION ALGORITHM FOR VISION-BASED PEDESTRIAN PROTECTION
    International Journal of Information Acquisition, 2008
    Co-Authors: Guanglin Ma, Su-birm Park, Alexander Ioffe, Stefan Müller-schneiders, Anton Kummert
    Abstract:

    This paper discusses the robust, real-time detection of stationary and moving pedestrians utilizing a single car-mounted Monochrome Camera. First, the system detects potential pedestrians above the ground plane by combining conventional Inverse Perspective Mapping (IPM)-based obstacle detection with the vertical 1D profile evaluation of the IPM detection result. Usage of the vertical profile increases the robustness of detection in low-contrast images as well as the detection of distant pedestrians significantly. A fast digital image stabilization algorithm is used to compensate for erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low-level pedestrian-oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented. A novel approach termed Pedestrian Detection Strip (PDS) is used to improve the calculation time by a factor of six compared to conventional approaches.

  • 3D Traffic Sign Tracking Using a Particle Filter
    2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
    Co-Authors: Mirko Meuter, Anton Kummert, Stefan Müller-schneiders
    Abstract:

    In recent years, there was much activity in the development of Camera based active safety systems to aid and to support the driver of a car. One application for such a system is the detection and classification of traffic signs. An important aspect of such a system is the tracking of traffic signs. We present a novel algorithm to track traffic signs in 3D using a single Monochrome Camera. The algorithm allows to use the constraint that the observed movement on the image plane is entirely caused by the host car movement, which is partially known from internal sensors. The usage of the sensor information improves the tracking process and allows a robust rejection of false positive detections. We also present a way to incorporate a shape cue directly from the image plane into the tracking process. First tests show good results in practice and indicate, that this kind of tracking makes a very valuable addition to a traffic sign detection system.

  • A Real Time Object Detection Approach Applied to Reliable Pedestrian Detection
    2007 IEEE Intelligent Vehicles Symposium, 2007
    Co-Authors: Su-birm Park, Alexander Ioffe, Stefan Müller-schneiders, Anton Kummert
    Abstract:

    This paper presents a robust real time obstacle and pedestrian detection algorithm, which is capable of handling the challenges of stationary as well as moving objects, utilizing a single car mounted Monochrome Camera. First, the system detects obstacles above the ground plane by obtaining a "virtual stereo system" through the usage of inverse perspective mapping. A fast digital image stabilization algorithm is used to compensate erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low level pedestrian segmentation algorithm is developed to extract bounding boxes of potential pedestrians. Furthermore a novel approach called the pedestrian detection strip is used to improve the calculation time by a factor of six compared to previous attempts. Experiments have been carried out by applying the proposed algorithm on prerecorded sequences as well as within a test vehicle and thus in a closed loop environment. The experimental results indicate a promising detection performance. Obstacles and pedestrians up to 50 meters away from the vehicle have been detected reliably at 64 frames per second on a 3 GHz PC.

Stephen Lin - One of the best experts on this subject based on the ideXlab platform.

  • A Prism-Mask System for Multispectral Video Acquisition
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
    Co-Authors: Xun Cao, Hao Du, Xin Tong, Qionghai Dai, Stephen Lin
    Abstract:

    This paper presents a prism-mask system for capturing multispectral videos. The system is composed of a triangular prism, a Monochrome Camera, and an occlusion mask. Incoming light beams from the scene are sampled by the occlusion mask, dispersed into their constituent spectra by the triangular prism, and then captured by the Monochrome Camera. Our system is capable of capturing frames with high spectral resolution at video rates. It also allows for different trade-offs between spectral and spatial resolution by adjusting the focal length of the Camera. We demonstrate multispectral video acquisition with various spectral resolutions and spatial resolutions, as well as different frame rates. The effectiveness of our system is further evaluated with several applications, including human skin detection, physical material recognition, video segmentation, RGB video generation, and illumination identification.

  • a prism based system for multispectral video acquisition
    International Conference on Computer Vision, 2009
    Co-Authors: Hao Du, Xin Tong, Xun Cao, Stephen Lin
    Abstract:

    In this paper, we propose a prism-based system for capturing multispectral videos. The system consists of a triangular prism, a Monochrome Camera, and an occlusion mask. Incoming light beams from the scene are sampled by the occlusion mask, dispersed into their constituent spectra by the triangular prism, and then captured by the Monochrome Camera. Our system is capable of capturing videos of high spectral resolution. It also allows for different tradeoffs between spectral and spatial resolution by adjusting the focal length of the Camera. We demonstrate the effectiveness of our system with several applications, including human skin detection, physical material recognition, and RGB video generation.