Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning

Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.

[1]  R.P. Avery,et al.  Length-based vehicle classification using images from uncalibrated video cameras , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[2]  Stan Sclaroff,et al.  Learning a Family of Detectors via Multiplicative Kernels , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Andrew Zisserman,et al.  An Exemplar Model for Learning Object Classes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[5]  Glenn Healey,et al.  Segmenting images using normalized color , 1992, IEEE Trans. Syst. Man Cybern..

[6]  Quming Zhou,et al.  Tracking and Classifying Moving Objects from Video , 2001 .

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

[8]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[9]  William T. Freeman,et al.  Latent hierarchical structural learning for object detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Qin Huang,et al.  Car detection using deformable part models with composite features , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[11]  Tieniu Tan,et al.  An Integrated Traffic and Pedestrian Model-Based Vision System , 1997, BMVC.

[12]  Yu-Feng Lin,et al.  Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[13]  P. Peer,et al.  Human skin color clustering for face detection , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[14]  Georgios Tziritas,et al.  Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis , 1999, IEEE Trans. Multim..

[15]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[16]  Kuo-Chin Fan,et al.  Vehicle Detection Using Normalized Color and Edge Map , 2007, IEEE Transactions on Image Processing.

[17]  Wenjie Zhu,et al.  Vehicle classification using sparse coding and spatial pyramid matching , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[18]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[19]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Alain Biem,et al.  Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM , 2011, IEEE Transactions on Neural Networks.

[21]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[22]  D Middleton,et al.  Assessing vehicle detection utilizing video image processing technology , 1996 .

[23]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[24]  Junjie Yan,et al.  The Fastest Deformable Part Model for Object Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  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).

[26]  Mohan M. Trivedi,et al.  Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning , 2016, IEEE Transactions on Intelligent Transportation Systems.

[27]  Hongbin Zha,et al.  Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection , 2016, IEEE Transactions on Intelligent Transportation Systems.

[28]  Alireza Ahmadyfard,et al.  An edge-based color-aided method for license plate detection , 2009, Image Vis. Comput..

[29]  Bir Bhanu,et al.  Dynamic Bayesian Networks for Vehicle Classification in Video , 2012, IEEE Transactions on Industrial Informatics.

[30]  Zehang Sun,et al.  On-road vehicle detection using Gabor filters and support vector machines , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[31]  J. D. Crisman,et al.  Vehicle detection in color images , 1999 .

[32]  P. C. Antonello,et al.  Multi-resolution vehicle detection using artificial vision , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[33]  Steven S. Beauchemin,et al.  Real-time vehicle detection and tracking using stereo vision and multi-view AdaBoost , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[34]  X. L. Huang,et al.  An Effective and Robust Multi-view Vehicle Classification Method Based on Local and Structural Features , 2016, 2016 IEEE Second International Conference on Multimedia Big Data (BigMM).

[35]  Qiang Wu,et al.  Learning-Based License Plate Detection Using Global and Local Features , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[36]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[37]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[38]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

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

[40]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[41]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[42]  Chengcui Zhang,et al.  Adaptive background learning for vehicle detection and spatio-temporal tracking , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[43]  A. Bozzoli,et al.  A tunable algorithm to update a reference image , 2000, Signal Process. Image Commun..

[44]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Jun-Wei Hsieh,et al.  Morphology-based license plate detection from complex scenes , 2002, Object recognition supported by user interaction for service robots.

[46]  Jie Zhou,et al.  Vehicle detection in static road images with PCA-and-Wavelet-Based classifier , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[47]  Qiang Chen,et al.  Contextualizing Object Detection and Classification , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Bo Wang,et al.  Multi-view vehicle detection in traffic surveillance combining HOG-HCT and deformarle part models , 2012, 2012 International Conference on Wavelet Analysis and Pattern Recognition.

[49]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  M. Tekalp,et al.  Automatic Vehicle Counting from Video for Traffic Flow Analysis , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[51]  Ping Tang,et al.  A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[53]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[54]  Jonathan Krause,et al.  Fine-Grained Car Detection for Visual Census Estimation , 2017, AAAI.

[55]  Paul E. Rybski,et al.  Real-time pedestrian detection with deformable part models , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[56]  Gregory Asner,et al.  Single-Species Detection With Airborne Imaging Spectroscopy Data: A Comparison of Support Vector Techniques , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[57]  Hervé Le Borgne,et al.  GPU deformable part model for object recognition , 2018, Journal of Real-Time Image Processing.