LDA Combined Depth Similarity and Gradient Features for Human Detection using a Time-of-Flight Sensor

Visual object detection is an important task for many research areas like driver assistance systems (DASs), industrial automation and various safety applications with human interaction. Since detection of pedestrians is a growing research area, different kinds of visual methods and sensors have been introduced to overcome this problem. This paper introduces new relational depth similarity features (RDSF) for the pedestrian detection using a Time-of-Flight (ToF) camera sensor. The new features are based on mean, variance, skewness and kurtosis values of local regions inside the depth image generated by the Time-of-Flight sensor. An evaluation between these new features, already existing relational depth similarity features using depth histograms of local regions and the well known histogram of oriented gradients (HOGs), which deliver very good results in the topic of pedestrian detection, will be presented. To incorporate more dimensional feature spaces, an existing AdaBoost algorithm, which uses linear discriminant analysis (LDA) for feature space reduction and new combination of already extracted features in the training procedure, will be presented too.

[1]  Dariu Gavrila,et al.  A new benchmark for stereo-based pedestrian detection , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  Eric O. Postma,et al.  Depth-based detection using haar-like features , 2012 .

[3]  Shiqi Yu,et al.  An attempt to pedestrian detection in depth images , 2011, 2011 Third Chinese Conference on Intelligent Visual Surveillance.

[4]  Mirko Meuter,et al.  An improved adaboost learning scheme using LDA features for object recognition , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[5]  Kai Oliver Arras,et al.  People detection in RGB-D data , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  John H. Maindonald,et al.  Modern Multivariate Statistical Techniques: Regression, Classification and Manifold Learning , 2009 .

[10]  Hironobu Fujiyoshi,et al.  Real-Time Human Detection Using Relational Depth Similarity Features , 2010, ACCV.

[11]  Thomas M. Breuel,et al.  Efficient implementation of local adaptive thresholding techniques using integral images , 2008, Electronic Imaging.

[12]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  Xiaojin Gong,et al.  A new depth descriptor for pedestrian detection in RGB-D images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[14]  Paul A. Viola,et al.  Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.