Object classification in traffic scenes using multiple spatio-temporal features

Object classification is a widely researched area in the field of computer vision. Lately there has been a lot of attention to appearance based models for representing objects. The most important feature of classifying objects such as pedestrians, vehicles, etc. in traffic scenes is that we have motion information available to us. The motion information presents itself in the form of temporal cues such as velocity and also as spatio-temporal cues such as optical flow, DHOG [6], etc. We propose a novel spatio-temporal feature based on covariance descriptors known as DCOV which captures complementary information to the DHOG feature. We present a combined classifier based on properties of tracked objects along with the DHOG and the DCOV features. We show based on experiments on the PETS 2001 dataset and two video sequences of bicycle and pedestrian traffic that the proposed classifier provides competent performance for distinguishing pedestrians, vehicles and bicyclists. Our method is also adaptive and benefits from the availability of more data for training. The classifier is also developed with real-time feasibility in mind.

[1]  Joachim Weickert,et al.  Reliable Estimation of Dense Optical Flow Fields with Large Displacements , 2000, International Journal of Computer Vision.

[2]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Anoop Cherian,et al.  Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet Divergence , 2011, 2011 International Conference on Computer Vision.

[4]  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).

[5]  Cordelia Schmid,et al.  Viewpoint-independent object class detection using 3D Feature Maps , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  W. Förstner,et al.  A Metric for Covariance Matrices , 2003 .

[7]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[8]  Charless C. Fowlkes,et al.  Discriminative Models for Multi-Class Object Layout , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  Yinhai Wang,et al.  Model‐Free Video Detection and Tracking of Pedestrians and Bicyclists , 2009, Comput. Aided Civ. Infrastructure Eng..

[11]  Rogério Schmidt Feris,et al.  An Integrated System for Moving Object Classification in Surveillance Videos , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[12]  Jitendra Malik,et al.  Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Biswajit Bose,et al.  Improving object classification in far-field video , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[16]  Lisa M. Brown,et al.  View independent vehicle/person classification , 2004, VSSN '04.

[17]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[18]  Alan J. Lipton Local Application of Optic Flow to Analyse Rigid versus Non-Rigid Motion , 1999 .

[19]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  N. Ayache,et al.  Log‐Euclidean metrics for fast and simple calculus on diffusion tensors , 2006, Magnetic resonance in medicine.

[21]  Shiming Xiang,et al.  Real-time Object Classification in Video Surveillance Based on Appearance Learning , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.