This paper describes an approach to the detection of vehicles in infrared images. Stable vehicle detection is important for future intelligent transport systems and is generally done by background subtraction and object modeling. To avoid the daylight-dependent and weather-dependent influences of varying illumination in visible images acquired with conventional ITV cameras, some researchers have been using infrared (IR) images. IR images make it easy to extract
foreground vehicle regions from background scenes, but their lack of clarity make object modeling difficult. We therefore propose a method that describes the internal pattern of each vehicle by using Gaussian mixture models (GMM) in the orientation-code image (OCI) space. Each pixel of an OCI has information about the maximum-gradient orientation of the IR image, not intensity information. Gradient orientation information does not depend on contrast and can describe the internal pattern structures of objects even in unclear IR images. We use the GMM to describe the topological structures of the internal patterns of vehicles. This approach can also eliminate the influences due to small differences between patterns. Evaluation tests with actual infrared video sequences have proved that the proposed algorithm provides stable vehicle detection.
KEYWORDS: Detection and tracking algorithms, 3D acquisition, Head, Data modeling, 3D metrology, Distance measurement, Image processing, Data processing, Feature extraction, Error analysis
This paper presents a novel fast and high-accurate 3-D registration algorithm. The ICP (Iterative Closest Point) algorithm converges all the 3-D data points of two data sets to the best matching points with minimum evaluation values. This algorithm is broadly used, because it has good availability to many applications. But, it needs many computational costs and it is very sensible to error values. Because, it uses whole data points of two data sets and least mean square optimization. We had proposed the M-ICP algorithm, which is an extension of the ICP algorithm based on modified M-estimation for realization of robustness against outlying gross noise.
The proposed algorithm named HM-ICP (Hierarchical M-ICP) is an extension of the M-ICP with selecting region for matching and hierarchical searching of selected regions. In this algorithm, we select regions using evaluation of variance for distance values in the target region and homogeneous topological mapping.
Some fundamental experiments utilizing real data sets of 3-D measurement show effectiveness of the proposed method. We achieved more than 4-digits number reduction of computational costs and confirmed less than 0.1% error to the measurement distance.
We propose Hierarchical Distributed Template Matching, which reduces the computational cost of template matching, while maintaining the same reliability as conventional template matching. To achieve cost reduction without loss of reliability, we first evaluate the correlation of shrunken images in order to select the maximum depth of the hierarchy. Then, for each level of hierarchy, we choose a small number of template points in the original template and build a sparse distributed template. The locations of the template points are optimized, so that they yield a distinct peak in the correlation score map. Experimental results demonstrate that our method can reduce the computational cost to less than 1/10 that of conventional hierarchical template matching. We also confirmed that the precision is 0.6 pixels.
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