A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for visual-context-aware object detection in still images
Computer Vision and Image Understanding
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Multimodal Detection and Tracking in Urban Environments
International Journal of Robotics Research
Pedestrian Detection and Tracking Using Three-dimensional LADAR Data
International Journal of Robotics Research
Classification and Semantic Mapping of Urban Environments
International Journal of Robotics Research
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
A Multilevel Mixture-of-Experts Framework for Pedestrian Classification
IEEE Transactions on Image Processing
A Minimal Solution for the Extrinsic Calibration of a Camera and a Laser-Rangefinder
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian detection in far infrared images
Integrated Computer-Aided Engineering
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In this work, a context-based multisensor system, applied to pedestrian detection in urban environments, is presented. The proposed system comprises three main processing modules: (i) a LIDAR-based module acting as the primary object detector, (ii) a module which supplies the system with contextual information obtained from a semantic map of the roads, and (iii) an image-based detection module, using sliding window detectors, with the role of validating the presence of pedestrians in the regions of interest generated by the LIDAR module. A Bayesian strategy is used to combine information from sensors onboard the vehicle ('local' information) with information contained in a digital map of the roads ('global' information). To support experimental analysis, a multisensor dataset, named the Laser and Image Pedestrian Detection dataset (LIPD), is used. The LIPD dataset was collected in an urban environment, under daylight conditions, using an electrical vehicle driven at low speed. A down-sampling method, using support vectors extracted from multiple linear SVMs, was used to reduce the cardinality of the training set and, as a consequence, to decrease the CPU time during the training process of the image-based classifiers. The performance of the system is evaluated, in terms of detection rate and the number of false positives per frame, using three image-detectors: a linear SVM, a SVM-cascade, and a benchmark method. Additionally, experiments are performed to assess the impact of contextual information on the performance of the detection system.