Abstract
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.
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References
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)
Guerrero-Gómez-Olmedo, R., López-Sastre, R.J., Maldonado-Bascón, S., Fernández-Caballero, A.: Vehicle tracking by simultaneous detection and viewpoint estimation. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013, Part II. LNCS, vol. 7931, pp. 306–316. Springer, Heidelberg (2013)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. IJRR 32(11), 1231–1237 (2013)
Caraffi, C., Vojir, T., Trefny, J., Sochman, J., Matas, J.: A system for real-time detection and tracking of vehicles from a single car-mounted camera. In: ITS Conference (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Sudowe, P., Leibe, B.: Efficient use of geometric constraints for sliding-window object detection in video. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds.) ICVS 2011. LNCS, vol. 6962, pp. 11–20. Springer, Heidelberg (2011)
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: NIPS (2010)
Fiaschi, L., Köthe, U., Nair, R., Hamprecht, F.A.: Learning to count with regression forest and structured labels. In: ICPR (2012)
Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: CVPR (2008)
Lu, W., Wang, S., Ding, X.: Vehicle detection and tracking in relatively crowded conditions. In: IEEE International Conference on Systems, Man, and Cybernetics (2009)
Jun, G., Aggarwal, J.K., Gökmen, M.: Tracking and segmentation of highway vehicles in cluttered and crowded scenes. In: IEEE Workshops on Applications of Computer Vision (2008)
Tamersoy, B., Aggarwal, J.K.: Robust vehicle detection for tracking in highway surveillance videos using unsupervised learning. In: AVSS (2009)
Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: BMVC (2012)
Arteta, C., Lempitsky, V., Noble, J., Zisserman, A.: Learning to detect partially overlapping instances. In: CVPR (2013)
Selinummi, J., Seppala, J., Yli-Harja, O., Puhakka, J.A.: Software for quantification of labeled bacteria from digital microscope images by automated image analysis. Biotechniques 39(6), 859–863 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008). http://www.vlfeat.org/
Acknowledgements
This work is supported by projects SPIP2014-1468, CCG2013/EXP-047, CCG2014/EXP-054, TEC2013-45183-R and IPT-2012-0808-370000.
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Guerrero-Gómez-Olmedo, R., Torre-Jiménez, B., López-Sastre, R., Maldonado-Bascón, S., Oñoro-Rubio, D. (2015). Extremely Overlapping Vehicle Counting. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_48
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DOI: https://doi.org/10.1007/978-3-319-19390-8_48
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