Human Silhouette Recognition with Fourier Descriptors
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
A Roadmap to the Integration of Early Visual Modules
International Journal of Computer Vision
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Appearance-Based Robot Discrimination Using Eigenimages
RoboCup 2006: Robot Soccer World Cup X
Boosting with temporal consistent learners: an application to human activity recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Feature set search space for fuzzyboost learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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In this paper we compare several optical flow based features in order to distinguish between humans and robots in a mixed human-robot environment. In addition, we propose two modifications to the optical flow computation: (i) a way to standardize the optical flow vectors, which relates the real world motions to the image motions, and (ii) a way to improve flow robustness to noise by selecting the sampling times as a function of the spatial displacement of the target in the world. We add temporal consistency to the flow-based features by using a temporalBoost algorithm. We compare combinations of: (i) several temporal supports, (ii) flow-based features, (iii) flow standardization, and (iv) flow sub-sampling. We implement the approach with better performance and validate it in a real outdoor setup, attaining real-time performance.