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VD (Vehicle Detection) is one of the main challenges in ADAS. The encountered vehicles can be of different orientations and positions, especially in scenarios such as crossroads. Traditional VD approaches have shown outstanding performance only with vehicle images of limited orientations and positions: head and tail of a vehicle, others such as side views have been shown to be detection challenged...
In this paper, we propose a novel approach to cluster incomplete images leveraging sparse subspace structure and total variation regularization. Sparse subspace clustering obtains a sparse representation coefficient matrix for input data points by solving an l1 minimization problem, and then uses the coefficient matrix to construct a sparse similarity graph over which spectral clustering is performed...
Moving vehicle detection in dynamical scene is a significant but challenging problem in these days. A new and effective approach to extract moving vehicles is proposed in this paper. In our method, Harris corner and Lucas-Kanade (L-K) optical flow was adopted to generalize feature-point optical flow field between two consecutive frames which obtained from monocular moving camera, and then vector quantization...
Using multi-vision system to implement the Point Positioning is more robust and accurate than using bi-vision system. However, multi-vision system is not feasible for real time Point Positioning; because multi-vision system mainly use optimization method. This paper proposes a fast Point Position method to address this problem. This method regard the average of all feet, which are determined by Back-Projection...
Given the low accuracy of visual tracking based on the routine particle filter caused by the particles poverty, this paper presents the novel sequence likelihood particle filter (SLPF) in which the particles are extracted from a discrete likelihood distribution. And the constructing procedure of the discrete likelihood is stated. And the weight updating formula of the sequence likelihood particle...
Image registration is an important task in the field of computer vision and pattern recognition. In this paper, we propose a robust sub-pixel registration algorithm which is based on multi-resolution and new edge detection interpolation method. After applying truncated window function to the images to be registration, the low-pass bands of the wavelet decomposition are applied to build the image pyramid...
This paper presents a method of insect recognition using computer vision technology. First, we extracted fourteen features from images of some species of insects. These features are rectangularity, elongation, roundness, eccentricity, sphericity, lobation, compactness and seven Hu moment invariants. Second, a machine learning algorithm named Random Trees was employed, to play a role of a classifier...
In order to achieve the goal of high accuracy and low cost in a visual localization system, we present a novel localization method based on four inexpensive video cameras. The method mainly consists of two parts: The “16-points interpolation algorithm” is proposed to enhance the accuracy of 2D coordinates of the detected target on the image plane. Another important aspect is that the Perpendicular...
This paper presents a feature recognition method based on randomized trees. We aim to improve the performance of Lepetit's work, whose actual results are very sensitive to large changes of viewpoint due to its limited ability of samples synthesizing and learning. We propose an approach to alleviate its limitation, which simulates the image appearance changes under actual viewpoint changes by applying...
The accuracy of corner detection is critical for many machine vision applications. A novel corner detector based on video is proposed in this paper. The corner detector can effectively constrain camera noise by using multiple frames from video. The kernel of this method is the similarity algorithm which including a special representation of binary image, a robust template, a simple similarity function...
This paper presents a FPGA-based auto focusing system for object ranging. It overcomes the disadvantage of manual focusing wise in traditional method of objects ranging. In our system, the distance of objects can be measured automatically by the auto focusing algorithm. We have experimentally demonstrated the effectiveness of this method. The best performance of our system is about 3% relative errors...
This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during...
This paper proposed a multi-cue based face tracking algorithm with the help of parallel multi-core processing. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of particle filter to improve the tracking performance...
In mineral processing, mineral flotation is a widely used traditional method for obtaining minerals. Normally, the froth surface is monitored and controlled by an operator who has good experiences for production optimisation, which is tedious and personal dependent. In order to overcome these disadvantages, a machine vision system was developed for monitoring froth surface variation. This paper presents...
Current mainstream vehicle recognition algorithms mainly depend on the synthesis of both appearance based and knowledge based features to identify the candidate objects. Whereas, because of the unpredictable complex noises in real world environments, the existences, quantification and the explanation for certain features are often ambiguous which makes current algorithm hard to fulfill the dilemmatic...
Detection and tracking of lane marking is essential for driving safety and intelligent vehicle. In this paper, an algorithm is presented which allows detection and tracking of multiple lane markings. Edge points cue is used to detect the lane marking and a road orientation estimation method is used to delete the edge lines which are impossible attribute to lane markings. In order to select the candidate...
This paper presents a novel non-rigid registration method for augmented reality applications using AAM and factorization method. The method can be divided into two stages: offline construction of 3D shape basis and online estimation of the 3D pose parameters together with the 3D shape coefficients. In offline stage, we get the training data with the use of the AAM algorithm, then we use factorization...
A monocular vision based detection algorithm is presented to detect rear vehicles. Our detection algorithm consist of two main steps: knowledge based hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, a shadow extraction method is proposed based on contrast sensitivity to extract regions of interest (ROI), it can effectively solve the problems caused...
A monocular vision based rear vehicle detection and tracking system is presented for Lane Change Assist (LCA), which does not need road boundary and lane information. Our algorithm extracts regions of interest (ROI) using the shadow underneath a vehicle, and accurately localizes vehicle regions in ROI by vehicle features such as symmetry, edge and shadow underneath vehicles. The algorithm realizes...
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