Fast and robust road sign detection in driver assistance systems

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness

Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from $O(N^4)$ to $O(N^3)$ (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.Comment: Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). arXiv admin note: substantial text overlap with arXiv:1803.0719

Gender Recognition and Appearance Description in Unconstrained Images of Human Body

Gender recognition has many useful applications, ranging from business intelligence, through to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. In this work, we propose a novel problem of gender recognition in articulated human body images acquired from an unconstrained environment in the real world. Our empirical study answers the question of whether gender recognition can be performed in articulated body images, and discovers important issues such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition. We also pursue data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases that have not been explored before for gender recognition. At the end of this work, we also present some preliminary results on the automatic description of the appearance of the upper body