ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Research of pedestrian detection for intelligent vehicle based on machine vision
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Tensor sparse coding for region covariances
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Journal of Real-Time Image Processing
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Pyramid center-symmetric local binary/trinary patterns for effective pedestrian detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Fast human detection based on enhanced variable size HOG features
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
A monocular human detection system based on EOH and oriented LBP features
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Visual tracking based on Log-Euclidean Riemannian sparse representation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Pedestrian detection and tracking using HOG and oriented-LBP features
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
Journal of Intelligent and Robotic Systems
Advances in matrix manifolds for computer vision
Image and Vision Computing
Fast pedestrian detection system with a two layer cascade of classifiers
Computers & Mathematics with Applications
A data-driven detection optimization framework
Neurocomputing
Creating robust high-throughput traffic sign detectors using centre-surround HOG statistics
Machine Vision and Applications
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Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally extracted features (e.g., local receptive fields, histogram of oriented gradients, and region covariance). Building upon the findings of our experiments, we propose a new, simpler pedestrian detector using the covariance features. Unlike the work in [1], where the feature selection and weak classifier training are performed on the Riemannian manifold, we select features and train weak classifiers in the Euclidean space for faster computation. To this end, AdaBoost with weighted Fisher linear discriminant analysis-based weak classifiers are designed. A cascaded classifier structure is constructed for efficiency in the detection phase. Experiments on different datasets prove that the new pedestrian detector is not only comparable to the state-of-the-art pedestrian detectors but it also performs at a faster speed. To further accelerate the detection, we adopt a faster strategy-multiple layer boosting with heterogeneous features-to exploit the efficiency of the Haar feature and the discriminative power of the covariance feature. Experiments show that, by combining the Haar and covariance features, we speed up the original covariance feature detector [1] by up to an order of magnitude in detection time with a slight drop in detection performance.