Abstract: A vehicle surroundings monitoring device includes a moving direction determining unit 20 which determines whether a physical body is moving in a traveling direction of a self vehicle or a transverse direction orthogonal to the traveling direction, from a change in a detection position of the physical body by a laser radar 8, wherein a classification determining unit 21 executes a first classification determining process of determining the physical body determined to be moving in the traveling direction as a four-wheel vehicle when dimensions of the physical body obtained from the detected position by the laser radar 8 is within a range of A1, and a second classification determining process of determining the physical body determined to be moving in the transverse direction as the four-wheel vehicle when the dimensions of the physical body obtained from the detected position by the laser radar 8 is within a range of B1.

Abstract: A vehicle vicinity monitoring apparatus detects a bicycle rider from infrared images. The vehicle vicinity monitoring apparatus converts the infrared images into binarized images, and calculates correlated errors of areas from a reference template. The vehicle vicinity monitoring apparatus judges an object of interest as the bicycle rider when the amplitude of the correlated errors changes periodically.

Abstract: A vehicle periphery monitoring device comprising a distance detecting unit which detects the distance between cameras and an object, an object image extracting unit which extracts a first image portion of the object from the acquired image, a filtering unit which performs a filtering process to the acquired image, in which a target edge width is set to be narrower as the assumed value of the width is smaller and the distance is longer, and in which a target edge direction is set to a direction orthogonal to the predetermined direction, and an object type identifying unit which identifies, on the basis of a shape of a second image portion extracted by the filtering process, whether or not the type of the object in real space corresponding to the second image portion is the specific type.

Abstract: A vehicle periphery monitoring device which determines type of an object with high accuracy, wherein the size of object regions that are set in each of the image representing the peripheral condition of the vehicle in each of two different points in time and that include the identical objects, are aligned on the basis of the distance from the vehicle to the object in each of the two different point in time. Further, local regions with the same arrangement pattern are set taking each of the object regions with aligned size as reference. Still further, the object is classified into the object class which corresponds to the arrangement pattern in the case where the degree of correlation between the local regions becomes equal to or larger than a threshold value.

Abstract: A vehicle periphery monitoring device comprising a distance detecting unit which detects the distance between cameras and an object, an object image extracting unit which extracts a first image portion of the object from the acquired image, a filtering unit which performs a filtering process to the acquired image, in which a target edge width is set to be narrower as the assumed value of the width is smaller and the distance is longer, and in which a target edge direction is set to a direction orthogonal to the predetermined direction, and an object type identifying unit which identifies, on the basis of a shape of a second image portion extracted by the filtering process, whether or not the type of the object in real space corresponding to the second image portion is the specific type.

Abstract: A vehicle periphery monitoring device which determines type of an object with high accuracy, wherein the size of object regions that are set in each of the image representing the peripheral condition of the vehicle [[1]] in each of two different points in time and that include the identical objects, are aligned on the basis of the distance from the vehicle to the object in each of the two different point in time. Further, local regions with the same arrangement pattern are set taking each of the object regions with aligned size as reference. Still further, the object is classified into the object class which corresponds to the arrangement pattern in the case where the degree of correlation between the local regions becomes equal to or larger than [[the]] a threshold value.