Learning to Detect Vehicles by Clustering Appearance Patterns

This paper studies efficient means in dealing with intracategory diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.

[1]  Armin B. Cremers,et al.  Laser-based segment classification using a mixture of bag-of-words , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Mohan M. Trivedi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Integrated Lane and Vehicle Detection, Localization, , 2022 .

[3]  Bernt Schiele,et al.  Detection and Tracking of Occluded People , 2014, International Journal of Computer Vision.

[4]  Bin Yang,et al.  Aggregate channel features for multi-view face detection , 2014, IEEE International Joint Conference on Biometrics.

[5]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[6]  Deva Ramanan,et al.  Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Anton van den Hengel,et al.  Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features , 2014, ECCV.

[8]  Bernt Schiele,et al.  Learning People Detectors for Tracking in Crowded Scenes , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Gang Hua,et al.  Accurate Object Detection with Location Relaxation and Regionlets Re-localization , 2014, ACCV.

[11]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[12]  Xiaofeng Ren,et al.  Discriminative Mixture-of-Templates for Viewpoint Classification , 2010, ECCV.

[13]  Luis Miguel Bergasa,et al.  Supervised learning and evaluation of KITTI's cars detector with DPM , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[14]  Charless C. Fowlkes,et al.  Do We Need More Training Data or Better Models for Object Detection? , 2012, BMVC.

[15]  Jitendra Malik,et al.  Deformable part models are convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ming Yang,et al.  Regionlets for Generic Object Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  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).

[18]  Martin Lauer,et al.  3D Traffic Scene Understanding From Movable Platforms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[20]  Mohan M. Trivedi,et al.  Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[21]  Horst Bischof,et al.  Accurate Object Detection with Joint Classification-Regression Random Forests , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[23]  Mohan M. Trivedi,et al.  Beyond just keeping hands on the wheel: Towards visual interpretation of driver hand motion patterns , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[24]  Wenze Hu,et al.  Modeling Occlusion by Discriminative AND-OR Structures , 2013, 2013 IEEE International Conference on Computer Vision.

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

[26]  Andrew Zisserman,et al.  Discriminative Sub-categorization , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Mohan M. Trivedi,et al.  Partially occluded vehicle recognition and tracking in 3D , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[28]  Seiichi Mita,et al.  Occlusion handling using discriminative model of trained part templates and conditional random field , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[29]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[30]  Song-Chun Zhu,et al.  Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model , 2014, ECCV.

[31]  David A. Forsyth,et al.  30Hz Object Detection with DPM V5 , 2014, ECCV.

[32]  Mohan M. Trivedi,et al.  Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics , 2015, IEEE Transactions on Intelligent Transportation Systems.

[33]  Akihiro Takeuchi,et al.  On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[34]  Noah Snavely,et al.  NYC3DCars: A Dataset of 3D Vehicles in Geographic Context , 2013, 2013 IEEE International Conference on Computer Vision.

[35]  Mohan M. Trivedi,et al.  Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[36]  Mohan M. Trivedi,et al.  Integrating motion and appearance for overtaking vehicle detection , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[37]  Mohan M. Trivedi,et al.  Fast and Robust Object Detection Using Visual Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[38]  Peter V. Gehler,et al.  Occlusion Patterns for Object Class Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Alexei A. Efros,et al.  How Important Are "Deformable Parts" in the Deformable Parts Model? , 2012, ECCV Workshops.

[40]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

[41]  Mohan M. Trivedi,et al.  Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Deva Ramanan,et al.  Analyzing 3D Objects in Cluttered Images , 2012, NIPS.

[43]  Ramakant Nevatia,et al.  Robust multi-view car detection using unsupervised sub-categorization , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[44]  Trevor Darrell,et al.  Discriminatively Activated Sparselets , 2013, ICML.

[45]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[46]  Bart De Schutter,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief , 2005 .

[47]  Mohan M. Trivedi,et al.  Vehicle Detection by Independent Parts for Urban Driver Assistance , 2013, IEEE Transactions on Intelligent Transportation Systems.

[48]  Mohan M. Trivedi,et al.  Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies , 2013, J. Electronic Imaging.

[49]  Dragomir Anguelov,et al.  Capturing Long-Tail Distributions of Object Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[51]  Greg Mori,et al.  From Subcategories to Visual Composites: A Multi-level Framework for Object Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[52]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[53]  Jonathon Shlens,et al.  Fast, Accurate Detection of 100,000 Object Classes on a Single Machine , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[54]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.