The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A non-myopic approach to visual search
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Robotics and Autonomous Systems
Putting Objects in Perspective
International Journal of Computer Vision
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Towards an Understanding of Hierarchical Architectures
IEEE Transactions on Autonomous Mental Development
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In this article, we explore the potential contribution of multimodal context information to object detection in an ''intelligent car''. The used car platform incorporates subsystems for the detection of objects from local visual patterns, as well as for the estimation of global scene properties (sometimes denoted ''scene context'' or just ''context'') such as the shape of the road area or the 3D position of the ground plane. Annotated data recorded on this platform is publicly available as the ''HRI RoadTraffic'' vehicle video dataset, which forms the basis for this investigation. In order to quantify the contribution of context information, we investigate whether it can be used to infer object identity with little or no reference to local patterns of visual appearance. Using a challenging vehicle detection task based on the ''HRI RoadTraffic'' dataset, we train selected algorithms (''context models'') to estimate object identity from context information alone. In the course of our performance evaluations, we also analyze the effect of typical real-world conditions (noise, high input dimensionality, environmental variation) on context model performance. As a principal result, we show that the learning of context models is feasible with all tested algorithms, and that object identity can be estimated from context information with similar accuracy as by relying on local pattern recognition methods. We also find that the use of basis function representations[1] (also known as ''population codes'') allows the simplest (and therefore most efficient) learning methods to perform best in the benchmark, suggesting that the use of context is feasible even in systems operating under strong performance constraints.