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    Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane

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    A method to detect obstacle-free paths in real-time which works as part of a cognitive navigation aid system for visually impaired people is proposed. It is based on the analysis of disparity maps obtained from a stereo vision system which is carried by the blind user. The presented detection method consists of a fuzzy logic system that assigns a certainty to be part of a free path to each group of pixels, depending on the parameters of a planar-model fitting. We also present experimental results on different real outdoor scenarios showing that our method is the most reliable in the sense that it minimizes the false positives rate.N. Ortigosa acknowledges the support of Universidad Politecnica de Valencia under grant FPI-UPV 2008 and Spanish Ministry of Science and Innovation under grant MTM2010-15200. S. Morillas acknowledges the support of Universidad Politecnica de Valencia under grant PAID-05-12-SP20120696.Ortigosa Araque, N.; Morillas Gómez, S. (2014). Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane. Journal of Intelligent and Robotic Systems. 75(2):313-330. https://doi.org/10.1007/s10846-013-9997-1S313330752Cai, L., He, L., Xu, Y., Zhao, Y., Yang, X.: Multi-object detection and tracking by stereovision. Pattern Recognit. 43(12), 4028–4041 (2010)Hikosaka, N., Watanabe, K., Umeda, K.: Obstacle detection of a humanoid on a plane using a relative disparity map obtained by a small range image sensor. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 3048–3053 (2007)Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Fast stixel computation for fast pedestrian detection. In: ECCV, CVVT workshop, October (2012)Huang, Y., Fu, S., Thompson, C.: Stereovision-based object segmentation for automotive applications. EURASIP J. Appl. Signal Process. 2005(14), 2322–2329 (2005)Duan, B.B., Liu, W., Fu, P.Y., Yang, C.Y., Wen, X.Z., Yuan, H.: Real-time on-road vehicle and motorcycle detection using a single camera. In: IEEE International Conference on Industrial Technology, pp. 579–584. IEEE (2009)Oliveira L, Nunes, U.: On integration of features and classifiers for robust vehicle detection. In: IEEE International Conference on Intelligent Transportation Systems, pp. 414–419. IEEE (2008)Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: A review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)Sun, H.J., Yang, J.Y.: Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Netw. Distrib. Process. 4555(1), 99–104 (2001)Hui, N.B., Pratihar, D.K.: Soft computing-based navigation schemes for a real wheeled robot moving among static obstacles. J. Intell. Robot. Syst. 51(3), 333–368 (2008)Menon, A., Akmeliawati, R., Demidenko, S.: Towards a simple mobile robot with obstacle avoidance and target seeking capabilities using fuzzy logic. In: Proceedings IEEE Instrumentation and Measurement Technology Conference, vol. 1–5, pp. 1003–1008 (2008)Moreno-Garcia, J., Rodriguez-Benitez, L., Fernandez-Caballero, A., Lopez, M.T.: Video sequence motion tracking by fuzzification techniques. Appl. Soft Comput. 10(1), 318–331 (2010)Nguyen, T.H., Nguyen, J.S., Pham, D.M., Nguyen, H.T.: Real-time obstacle detection for an autonomous wheelchair using stereoscopic cameras. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007(1), 4775–4778 (2007)Nguyen, J.S., Nguyen, T.H., Nguyen, H.T.: Semi-autonomous wheelchair system using stereoscopic cameras. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1–20, pp. 5068–5071 (2009)Grosso, E., Tistarelli, M.: Active/dynamic stereo vision. IEEE Trans. Pattern Anal. Mach. Intell. 17(9), 868–879 (1995)Kubota, S., Nakano, T., Okamoto, Y.: A global optimization for real-time on-board stereo obstacle detection systems. In: IEEE Intelligent Vehicles Symposium, pp. 7–12. IEEE (2007)Ortigosa, N., Morillas, S., Peris-Fajarnés, G., Dunai, L.: Fuzzy free path detection based on dense disparity maps obtained from stereo cameras. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 20(2), 245–259 (2012)Murray, D., Little, J.J.: Using real-time stereo vision for mobile robot navigation. Auton. Robot. 8(2), 161–171 (2000)Badino, H., Mester, R., Vaudrey, T., Franke, U.: Stereo-based free space computation in complex traffic scenarios. In: IEEE Southwest Symposium on Image Analysis & Interpretation, pp. 189–192 (2008)Hoilund, C., Moeslund, T.B., Madsen, C.L., Trivedi, M.M.: Free space computation from stochastic occupancy grids based on iconic kalman filtered disparity maps. In: Proceedings International Conference on Computer Vision Theory and Applications, vol. 1, pp. 164–167 (2010)Franke, U., Joos, A.: Real-time stereo vision for urban traffic scene understanding. In: IEEE Intelligent Vehicles Symposium, pp. 273–278. IEEE (2000)Wedel, A., Badino, H., Rabe, C., Loose, H., Franke, U., Cremers, D.: B-spline modeling of road surfaces with an application to free-space estimation. IEEE Trans. Intell. Transp. Syst. 10(4), 572–583 (2009)Vergauwen, M., Pollefeys, M., Van Gool, L.: A stereo-vision system for support of planetary surface exploration. Mach. Vis. Appl. 14(1), 5–14 (2003)Tarel, J.P., Leng, S.S., Charbonnier, P.: Accurate and robust image alignment for road profile reconstruction. In: IEEE International Conference on Image Processing, pp. 365–368. IEEE (2007)Kostavelis, I., Gasteratos, A.: Stereovision-based algorithm for obstacle avoidance. In: Lecture Notes in Computer Science, pp. 195–204. Intelligent Robotics and Applications (2009)Cerri, P., Grisleri, P.: Free space detection on highways using time correlation between stabilized sub-pixel precision ipm images. In: IEEE International Conference on Robotics and Automation, pp. 2223–2228. IEEE (2005)Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereo vision on non-flat road geometry through v-disparity representation. In: IEEE Intelligent Vehicle Symposium, pp. 646–651. INRIA (2002)Ortigosa, N., Morillas, S., Peris-Fajarnés, G., Dunai, L.: Disparity maps for free path detection. In: Proceedings International Conference on Computer Vision Theory and Applications, vol. 1, pp. 310–315 (2010)Ortigosa, N., Morillas, S., Peris-Fajarnés, G.: Obstacle-free pathway detection by means of depth maps. J. Intell. Robot. Syst. 63(1), 115–129 (2011)http://www.casblip.comBach y Rita, P., Collins, C., Sauders, B., White, B., Scadden, L.: Vision substitution by tactile image projection. Nature 221, 963964 (1969)Sampaio, E., Maris, S., Bach y Rita, P.: Brain plasticity: visual acuity of blind persons via the tongue. Brain Res. 908, 204207 (2001)http://www.seeingwithsound.comCapelle, C., Trullemans, C., Arno, P., Veraart, C.: A real-time experimental prototype for enhancement of vision rehabilitation using auditory substitution. IEEE Trans. Biomed. Eng. 45, 12791293 (1998)Lee, S.W., Kang, S.K., Lee, S.A.: A walking guidance system for the visually impaired. Int. J. Pattern Recognit. 22, 11711186 (2008)Chen, C.L., Liao, Y.F., Tai, C.L.: Image-to-midi mapping based on dynamic fuzzy color segmentation for visually impaired people. Pattern Recognit. Lett. 32, 549–560 (2011)Lombardi, P., Zanin, M., Messelodi, S.: Unified stereovision for ground, road, and obstacle detection. In: Proceedings on the Intelligent Vehicles Symposium, 2005, pp. 783–788. IEEE (2005)Yu, Q., Araujo, H., Wang, H.: Stereo-vision based real time obstacle detection for urban environments. In: Proceedings on the International Conference of Advanced Robotics, vol. 1, pp. 1671–1676 (2003)Benenson, R., Timofte, R., Van Gool, L.: Stixels estimation without depth map computation. In: ICCV, CVVT workshop (2011)Li, X., Yao, X., Murphey, Y.L., Karlsen, R., Gerhart, G.: A real-time vehicle detection and tracking system in outdoor traffic scenes. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2, pp. 761–764 (2004)Zhang, Z.Y.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)Dhond, U.R., Aggarwal, J.K.: Structure from stereo: a review. IEEE Trans. Syst. Man Cybern. 19, 1489–1510 (1989)Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1/2/3), 7–42 (2002)Middlebury Stereo Vision Page. http://vision.middlebury.edu/stereo/Birchfield, S., Tomasi, C.: Depth discontinuities by pixel-to-pixel stereo. Int. J. Comput. Vis. 17(3), 269–293 (1999)Lawrence Zitnick, C., Bing Kang, S.: Stereo for image-based rendering using image over-segmentation. Int. J. Comput. Vis. 75(1), 49–65 (2007)Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Int. J. Comput. Vis. 70(1), 41–54 (2006)Yang, Q., Wang, L., Yang, R., Stewnius, H., Nistr, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 492–504 (2009)Gehrig, S., Eberli, F., Meyer, T.: A real-time low-power stereo vision engine using semi-global matching. Lect. Notes Comput. Sci. 5815/2009, 134–143 (2009)Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U., Cremers, D.: Stereoscopic scene flow computation for 3d motion understanding. Int. J. Comput. Vis. 95, 29–51 (2011)Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)Leung, C., Appleton, B., Sun, C.: Iterated dynamic programming and quadtree subregioning for fast stereo matching. Image Vis. Comput. 26(10), 1371–1383 (2008)Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, ISBN: 0521540518 (2004)Spiegel, M.R., Stepthens, L.J.: Statistics, 4th edn. Mc Graw Hill (2008)Kerre, E.E.: Fuzzy sets and approximate reasoning. Xian Jiaotong University Press (1998)Dubois, D., Prade, H.: Fuzzy sets and systems: theory and applications. Academic Press, New York (1980)Lee, C.C.: Fuzzy logic in control systems: Fuzzy logic controller-parts 1 and 2. IEEE Trans. Syst. Man Cybern. 20(2), 404–435 (1990)Fodor, J.C.: A new look at fuzzy-connectives. Fuzzy Sets Syst. 57(2), 141–148 (1993)Nalpantidis, L., Gasteratos, A.: Stereo vision for robotic applications in the presence of non-ideal lightning conditions. Image Vis. Comput. 28(6), 940–951 (2010

    In-Band Disparity Compensation for Multiview Image Compression and View Synthesis

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    Robust Dense Mapping for Large-Scale Dynamic Environments

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    We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation (ICRA), 201

    Robust pedestrian detection and tracking in crowded scenes

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    In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

    Depth Image Processing for Obstacle Avoidance of an Autonomous VTOL UAV

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    We describe a new approach for stereo-based obstacle avoidance. This method analyzes the images of a stereo camera in realtime and searches for a safe target point that can be reached without collision. The obstacle avoidance system is used by our unmanned helicopter ARTIS (Autonomous Rotorcraft Testbed for Intelligent Systems) and its simulation environment. It is optimized for this UAV, but not limited to aircraft systems
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