Pedestrian lane detection in unstructured scenes for assistive navigation

Automatic detection of the pedestrian lane in a scene is an important task in assistive and autonomous navigation. This paper presents a vision-based algorithm for pedestrian lane detection in unstructured scenes, where lanes vary significantly in color, texture, and shape and are not indicated by any painted markers. In the proposed method, a lane appearance model is constructed adaptively from a sample image region, which is identified automatically from the image vanishing point. This paper also introduces a fast and robust vanishing point estimation method based on the color tensor and dominant orientations of color edge pixels. The proposed pedestrian lane detection method is evaluated on a new benchmark dataset that contains images from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results are presented which demonstrate its efficiency and robustness in comparison with several existing methods

Vanishing Point Detection By Segment Clustering On The Projective Space

The analysis of vanishing points on digital images provides strong cues for inferring the 3D structure of the depicted scene and can be exploited in a variety of computer vision applications. In this paper, we propose a method for estimating vanishing points in images of architectural environments that can be used for camera calibration and pose estimation, important tasks in large-scale 3D reconstruction. Our method performs automatic segment clustering in projective space - a direct transformation from the image space - instead of the traditional bounded accumulator space. Since it works in projective space, it handles finite and infinite vanishing points, without any special condition or threshold tuning. Experiments on real images show the effectiveness of the proposed method. We identify three orthogonal vanishing points and compute the estimation error based on their relation with the Image of the Absolute Conic (IAC) and based on the computation of the camera focal length. © 2012 Springer-Verlag.6554 LNCSPART 2324337Zeng, X., Wang, Q., Xu, J., MAP Model for Large-scale 3D Reconstruction and Coarse Matching for Unordered Wide-baseline Photos British Machine Vision Conference (2008)Jang, K.H., Jung, S.K., Practical modeling technique for large-scale 3D building models from ground images (2009) Pattern Recognition Letters, 30 (10), pp. 861-869Lee, S.C., Jung, S.K., Nevatia, R., Automatic Integration of Facade Textures into 3D Building Models with a Projective Geometry Based Line Clustering (2002) USC Computer VisionTeller, S., Antone, M., Bodnar, Z., Bosse, M., Coorg, S., Jethwa, M., Master, N., Calibrated, Registered Images of an Extended Urban Area (2003) International Journal of Computer Vision, 53 (1), pp. 93-107Wilczkowiak, M., Sturm, P., Boyer, E., Using Geometric Constraints through Parallelepipeds for Calibration and 3D Modeling (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2), pp. 194-207Wang, G., Tsui, H.-T., Hu, Z., Wu, F., Camera calibration and 3D reconstruction from a single view based on scene constraints (2005) Image and Vision Computing, 23 (3), pp. 311-323Wang, G., Tsu, H.-T., Wu, Q.M.J., What can we learn about the scene structure from three orthogonal vanishing points in images (2009) Pattern Recognition Letters, 30 (3), pp. 192-202Canny, J., A computational approach to edge detection (1986) IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6), pp. 679-698Duda, R.O., Hart, P.E., Use of the Hough transformation to detect lines and curves in pictures (1972) Communications of the ACM, 15 (1), pp. 11-15Barnard, S.T., Interpreting perspective images (1983) Artificial Intelligence, 21 (4), pp. 435-462Tuytelaars, T., Van Gool, L.J., Proesmans, M., Moons, T., A Cascaded Hough Transform as an Aid in Aerial Image Interpretation (1998) International Conference on Computer Vision, pp. 67-72Shufelt, J.A., Performance Evaluation and Analysis of Vanishing Point Detection Techniques (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (3), pp. 282-288Almansa, A., Desolneux, A., Vamech, S., Vanishing Point Detection without Any A Priori Information (2003) IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (4), pp. 502-507Rother, C., A New Approach for Vanishing Point Detection in Architectural Environments British Machine Vision Conference (2000)McLean, G.F., Kotturi, D., Vanishing Point Detection by Line Clustering (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (11), pp. 1090-1095Tardif, J.-P., Non-Iterative Approach for Fast and Accurate Vanishing Point Detection (2009) International Conference on Computer Vision, pp. 1250-1257Desolneux, A., Moisan, L., Morel, J.-M., Edge Detection by Helmholtz Principle (2001) Journal of Mathematical Imaging and Vision, 14 (3), pp. 271-284Stolfi, J., (1991) Oriented Projective Geometry: A Framework for Geometric Computations, , Academic PressMardia, K.V., Jupp, P.E., (1999) Directional Statistics, , John Wiley and SonsDenis, P., Elder, J.H., Estrada, F.J., Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery (2008) LNCS, 5303, pp. 197-210. , Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. Springer, Heidelber