Seiichi Mita

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This paper proposes a novel method for multivehicle detection and tracking using a vehicle-mounted monocular camera. In the proposed method, the features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOGs). The detection algorithm combines both(More)
This paper presents a robust stereo-vision-based drivable road detection and tracking system that was designed to navigate an intelligent vehicle through challenging traffic scenarios and increment road safety in such scenarios with advanced driver-assistance systems (ADAS). This system is based on a formulation of stereo with homography as a maximum a(More)
Visual-based approaches have been extensively studied for on-road vehicle detection; however, it faces great challenges as the visual appearance of a vehicle may greatly change across different viewpoints and as a partial observation sometimes happens due to occlusions from infrastructure or scene dynamics and/or a limited camera vision field. This paper(More)
Vision-based object detection using camera sensors is an essential piece of perception for autonomous vehicles. Various combinations of features and models can be applied to increase the quality and the speed of object detection. A well-known approach uses histograms of oriented gradients (HOG) with deformable models to detect a car in an image [15]. A(More)
The accurate detection and recognition of traffic lights is important for autonomous vehicle navigation and advanced driver aid systems. In this paper, we present a traffic light recognition algorithm for varying illumination conditions using computer vision and machine learning. More specifically, a convolutional neural network is used to extract and(More)
Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security and surveillance etc. Generally, pedestrian detection is conducted for images in visible spectrum, which are not suitable for night time detection. Infrared(More)
Road detection is one of the key issues for the implementation of intelligent vehicles. In this paper, we present a drivable road region detection method using homography estimation and efficient belief propagation. In the method, each pixel in stereo images is assigned a label by minimizing an energy function that accounts for the planar road region, which(More)
This paper presents a vision-based approach with unsupervised learning for robust, accurate and stable detection of the drivable road to deal with autonomous driving in changing environments. This approach is based on a formulation of stereo with homography as a Maximum A Posteriori (MAP) problem in a Markov Random Field (MRF). Under this formulation, we(More)
We construct a two-dimensional systolic array implementing the Berlekamp-Massey-Sakata (BMS) algorithm to provide error-locator polynomials for codes on selected algebraic curves. This array is constructed by introducing some new polynomials in order to increase the parallelism of the algorithm. The introduced polynomials are used in the majority logic(More)
We propose a novel encoding scheme for algebraic codes such as codes on algebraic curves, multidimensional cyclic codes, and hyperbolic cascaded Reed-Solomon codes and present numerical examples. We employ the recurrence from the Grobner basis of the locator ideal for a set of rational points and the two- dimensional inverse discrete Fourier transform. We(More)