Detection and Orientation Estimation for Cyclists by Max Pooled Features

Wei Tian, Martin Lauer

2017

Abstract

In this work we propose a new kind of HOG feature which is built by the max pooling operation over spatial bins and orientation channels in multilevel and can efficiently deal with deformation of objects in images. We demonstrate its invariance against both translation and rotation in feature levels. Experimental results show a great precision gain on detection and orientation estimation for cyclists by applying this new feature on classical cascaded detection frameworks. In combination of the geometric constraint, we also show that our system can achieve a real time performance for simultaneous cyclist detection and its orientation estimation.

References

  1. Behley, J., Steinhage, V., and Cremers, A. B. (2013). Laserbased segment classification using a mixture of bagof-words. In IEEE Conference on Intelligent Robots and Systems.
  2. Benenson, R., Mathias, M., Timofte, R., and Van Gool, L. (2012). Pedestrian detection at 100 frames per second. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  3. Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., and Urtasun, R. (2016). Monocular 3D Object Detection for Autonomous Driving. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  4. Chen, X., Kundu, K., Zhu, Y., Berneshawi, A. G., Ma, H., Fidler, S., and Urtasun, R. (2015). 3D Object Proposals for Accurate Object Class Detection. In Advances in Neural Information Processing Systems (NIPS).
  5. Cho, H., Rybski, P., and Zhang, W. (2010). Vision-based Bicycle Detection and Tracking using a Deformable Part Model and an EKF Algorithm. In IEEE Conference on Intelligent Transportation Systems.
  6. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  7. Dharmaraju, R., Noyce, D. A., and Lehman, J. D. (2002). An Evaluation of Technologies for Automated Detection and Classification of Pedestrians and Bicyclists.
  8. Dollár, P., Tu, Z., Perona, P., and Belongie, S. (2009). Integral Channel Features. In British Machine Vision Conference (BMVC).
  9. Dollár, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian Detection: An Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 34.
  10. EU (2015). Traffic Safety Basic Facts 2015.European Road Safety Observatory.
  11. Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D. (2010). Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 32:1627-1645.
  12. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  13. Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  14. Girshick, R., Iandola, F., Darrell, T., and Malik, J. (2015). Deformable Part Models are Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  15. Gonzlez, A., Villalonga, G., Xu, J., Vzquez, D., Amores, J., and Lpez, A. M. (2015). Multiview random forest of local experts combining rgb and lidar data for pedestrian detection. In IEEE Intelligent Vehicles Symposium (IV).
  16. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
  17. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional Architecture for Fast Feature Embedding. ACM international conference on Multimedia.
  18. Krogmeier, J. V. and Bullock, D. M. (2008). Inductive Loop Detection of Bicycles and Inductive Loop Signature Processing for Travel Time Estimation. Statewide Wireless Communications Project, 2.
  19. Li, T., Cao, X., and Xu, Y. (2010). An effective crossing cyclist detection on a moving vehicle. In World Congress on Intelligent Control and Automation (WCICA).
  20. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
  21. Paisitkriangkrai, S., Shen, C., and van den Hengel, A. (2015). Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
  22. Pape, M. (2015). Cycling mobility in the EU. Members' Research Service, 557013.
  23. Pepik, B., Stark, M., Gehler, P., and Schiele, B. (2015). Multi-View and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
  24. Qui, Z., Yao, D., Zhang, Y., Ma, D., and Liu, X. (2003). The study of the detection of pedestrian and bicycle using image processing. In IEEE Conference on Intelligent Transportation Systems.
  25. Rajaram, R. N., Ohn-Bar, E., and Trivedi, M. M. (2015). An Exploration of Why and When Pedestrian Detection Fails. In IEEE Conference on Intelligent Transportation Systems.
  26. Rogers, S. and Papanikolopoulos, N. (2000). Counting bicycles using computer vision. In IEEE Conference on Intelligent Transportation Systems.
  27. Sudowe, P. and Leibe, B. (2011). Efficient use of geometric constraints for sliding-window object detection in video. In Computer Vision Systems, volume 6962, pages 11-20. Springer Berlin Heidelberg.
  28. Takahashi, K., Kuriya, Y., and Morie, T. (2010). Bicycle detection using pedaling movement by spatiotemporal gabor filtering. In TENCON 2010 - IEEE Region 10 Conference, pages 918-922.
  29. Tian, W. and Lauer, M. (2015a). Fast and Robust Cyclist Detection for Monocular Camera Systems. In International joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP).
  30. Tian, W. and Lauer, M. (2015b). Fast Cyclist Detection by Cascaded Detector and Geometric Constraint. IEEE Conference on Intelligent Transportation Systems.
  31. Viola, P. and Jones, M. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision, (2):137-154.
  32. Wang, D. Z. and Posner, I. (2015). Voting for Voting in Online Point Cloud Object Detection. In Robotics: Science and Systems.
  33. Xiang, Y., Choi, W., Lin, Y., and Savarese, S. (2016). Subcategory-aware convolutional neural networks for object proposals and detection. arXiv:1604.04693.
  34. Xiong, W., Du, B., Zhang, L., Hu, R., Bian, W., Shen, J., and Tao, D. (2015). R2fp: Rich and robust feature pooling for mining visual data. In 2015 IEEE International Conference on Data Mining (ICDM).
  35. Yang, F., Choi, W., and Lin, Y. (2016). Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. InIEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  36. Yebes, J. J., Bergasa, L. M., Arroyo, R., and Lzaro, A. (2014). Supervised learning and evaluation of KITTI's cars detector with DPM. In IEEE Intelligent Vehicles Symposium (IV).
  37. Zhang, S., Benenson, R., Omran, M., Hosang, J. H., and Schiele, B. (2016). How Far are We from Solving Pedestrian Detection? Computer Vision and Pattern Recognition (CVPR).
  38. Zhang, S., Benenson, R., and Schiele, B. (2015). Filtered channel features for pedestrian detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  39. Zou, W., Wang, X., Sun, M., and Lin, Y. (2014). Generic Object Detection with Dense Neural Patterns and Regionlets. In British Machine Vision Conference (BMVC).
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Paper Citation


in Harvard Style

Tian W. and Lauer M. (2017). Detection and Orientation Estimation for Cyclists by Max Pooled Features . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 17-26. DOI: 10.5220/0006085500170026


in Bibtex Style

@conference{visapp17,
author={Wei Tian and Martin Lauer},
title={Detection and Orientation Estimation for Cyclists by Max Pooled Features},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006085500170026},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Detection and Orientation Estimation for Cyclists by Max Pooled Features
SN - 978-989-758-226-4
AU - Tian W.
AU - Lauer M.
PY - 2017
SP - 17
EP - 26
DO - 10.5220/0006085500170026