Abstract
In smart-cities, computer vision has the potential to dramatically improve the quality of life of people suffering of visual impairments. In this field, we have been working on a wearable mobility aid aimed at detecting in real-time obstacles in front of a visually impaired. Our approach relies on a custom RGBD camera, with FPGA on-board processing, worn as traditional eyeglasses and effective point-cloud processing implemented on a compact and lightweight embedded computer. This latter device also provides feedback to the user by means of an haptic interface as well as audio messages. In this paper we address crosswalk recognition that, as pointed out by several visually impaired users involved in the evaluation of our system, is a crucial requirement in the design of an effective mobility aid. Specifically, we propose a reliable methodology to detect and categorize crosswalks by leveraging on point-cloud processing and deep-learning techniques. The experimental results reported, on 10000+ frames, confirm that the proposed approach is invariant to head/camera pose and extremely effective even when dealing with large occlusions typically found in urban environments.
Chapter PDF
Similar content being viewed by others
References
Haselhoff, A., Kummert, A.: On visual crosswalk detection for driver assistance systems. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 883–888, June 2010
Wu, T., Ranganathan, A.: A practical system for road marking detection and recognition. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 25–30, June 2012
Mancini, A., Frontoni, E., Zingaretti, P.: Automatic road object extraction from mobile mapping systems. In: 2012 IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications (MESA), pp. 281–286, July 2012
Hata, A., Wolf, D.: Road marking detection using lidar reflective intensity data and its application to vehicle localization. In: 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 584–589, October 2014
Suzuki, S., Raksincharoensak, P., Shimizu, I., Nagai, M., Adomat, R.: Sensor fusion-based pedestrian collision warning system with crosswalk detection. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 355–360, June 2010
Ishizaki, R., Morimoto, M., Fujii, K.: An evaluation method of driving behavior by in-vehicle data camera. In: 2012 Fifth International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 293–297, November 2012
Shioyama, T., Wu, H., Nishibe, Y., Nakamura, N., Kitawaki, S.: Image analysis of crosswalk. In: proceedings of the 11th International Conference on Image Analysis and Processing, pp. 168–173, September 2001
Ivanchenko, V., Coughlan, J., Shen, H.: Detecting and locating crosswalks using a camera phone. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8, June 2008
Ahmetovic, D., Bernareggi, C., Gerino, A., Mascetti, S.: Zebrarecognizer: efficient and precise localization of pedestrian crossings. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 2566–2571, August 2014
Radvanyi, M., Varga, B., Karacs, K.: Advanced crosswalk detection for the bionic eyeglass. In: 2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA), pp. 1–5, February 2010
Wang, S., Tian, Y.: Detecting stairs and pedestrian crosswalks for the blind by rgbd camera. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 732–739, October 2012
Murali, V.N., Coughlan, J.M.: Smartphone-based crosswalk detection and localization for visually impaired pedestrians. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–7, July 2013
Mattoccia, S., Macri’, P.: 3d glasses to improve autonomous mobility of people visually impaired. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV Workshop. LNCS, vol. 8927, pp. 539–554. Springer, Switzerland (2014)
Mattoccia, S., Marchio, I., Casadio, M.: A compact 3d camera suited for mobile and embedded vision applications. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 195–196, June 2014
Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)
Hard-Kernel: Odroid u3. http://hardkernel.com/main/main.php
Choi, S., Kim, T., Yu, W.: Performance evaluation of ransac family. In: BMVC (2009)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: Proc. International Conference on Computer Vision (ICCV 2009). IEEE (2009)
Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Poggi, M., Nanni, L., Mattoccia, S. (2015). Crosswalk Recognition Through Point-Cloud Processing and Deep-Learning Suited to a Wearable Mobility Aid for the Visually Impaired. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-23222-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23221-8
Online ISBN: 978-3-319-23222-5
eBook Packages: Computer ScienceComputer Science (R0)