Extremely Overlapping Vehicle Counting

The challenging problem that we explore in this paper is to precisely estimate the number of vehicles in an image of a traffic congestion situation. We start introducing TRANCOS, a novel database for extremely overlapping vehicle counting. It provides more than 1200 images where the number of vehicles and their locations have been annotated. We establish a clear experimental setup which will let others evaluate their own vehicle counting approaches. We also propose a novel evaluation metric, the Grid Average Mean absolute Error (GAME), which overcomes the limitations of previously proposed metrics for object counting. Finally, we perform an experimental validation, using the proposed TRANCOS dataset, for two types of vehicle counting strategies: counting by detection; and counting by regression. Our results show that counting by regression strategies are more precise localizing and estimating the number of vehicles. The TRANCOS database and the source code for reproducing the results are available at http://agamenon.tsc.uah.es/Personales/rlopez/data/trancos.

[1]  Olli Yli-Harja,et al.  Software for quantification of labeled bacteria from digital microscope images by automated image analysis. , 2005, BioTechniques.

[2]  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).

[3]  Shaogang Gong,et al.  Feature Mining for Localised Crowd Counting , 2012, BMVC.

[4]  Xiaoqing Ding,et al.  Vehicle detection and tracking in relatively crowded conditions , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Jake K. Aggarwal,et al.  Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[6]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Jake K. Aggarwal,et al.  Tracking and Segmentation of Highway Vehicles in Cluttered and Crowded Scenes , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[8]  Ullrich Köthe,et al.  Learning to count with regression forest and structured labels , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

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

[11]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[12]  Antonio Fernández-Caballero,et al.  Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation , 2013, IWINAC.

[13]  Andrew Zisserman,et al.  Learning to Detect Partially Overlapping Instances , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[15]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[16]  Bastian Leibe,et al.  Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video , 2011, ICVS.

[17]  Jiri Matas,et al.  A system for real-time detection and tracking of vehicles from a single car-mounted camera , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.