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The maximum consensus problem lies at the core of several important computer vision applications as it is one of the most popular criteria for robust estimation. Although considerable efforts have been devoted to solving this problem, exact algorithms are still impractical for real-world data. Randomized hypothesize-and-test approaches such as RANSAC and its variants are therefore still the key players...
Stereo matching is important in the area of computer vision and photogrammetry. We present a stereo matching algorithm to refine depth map by using stereo image pair. The reference image is segmented by using hill-climbing algorithm and Scale Invariant Feature Transform (SIFT) feature descriptor with Sum of Absolute Difference (SAD) local stereo matching is performed. Next, we extract a set of disparity...
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level...
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation,...
Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively evaluated our proposal on 23 confidence measures, including...
Crowd analysis on video recordings is an important research area currently. In this work, a combined crowd density estimation method is presented to overcome this problem. To improve the accuracy of the system two different estimators run simultaneously and a blob is marked as a person only if both estimators mark it as person. One of the main problems in crowd density estimation is occlusion. To...
Automated affective computing in the wild is a challenging task in the field of computer vision. This paper presents three neural network-based methods proposed for the task of facial affect estimation submitted to the First Affect-in-the-Wild challenge. These methods are based on Inception-ResNet modules redesigned specifically for the task of facial affect estimation. These methods are: Shallow...
Automatic and accurate human upper-body detection and orientation estimation have great practical value in several computer vision applications. Most previous works on human upper-body orientation estimation assume that the human upper-body region is already detected and aligned. However, this is not the case in many real-world scenarios. Additional human detector is essential which is usually much...
For low density crowd, the statistical information of pixels and feature points can reflect the change of crowd density. Therefore, pixels and corners are fused in this paper, then, SVR is used to learn the corresponding relationship between feature and the number of people. While PSO is used to optimize the choice of parameters C and gamma in SVR. The experimental results show that the SVR optimized...
Image processing is an inevitable tool for visual tracking. Visual object tracking is a very hot area of research in the computer vision. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and in general, deal with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the...
Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology, is different than exocentric (third-person) vision in some distinguishable ways, one of which being that the camera wearer is generally not visible in the video frames. Recent work has been done on action and object recognition in egocentric videos, as well as work on biometric extraction...
Human behavior analysis based on surveillance camera is one of hot topics in security, marketing as well as computer vision and pattern recognition, and these are useful for commercial facilities such as convenience stores or book stores. In general, since surveillance camera is placed on the ceiling near store wall to monitor customer behaviors, the majority of this research utilize human model adapted...
Crowd density analysis is very imperative for intellectual video surveillance to help in management and control of crowds for safety. In recent years, more and more datasets dedicated to crowd density estimation, crowd analysis and anomaly detection in crowded scenes have been created. The use of these dataset allows us to compare different crowd density estimation methods with the same input data...
In this paper, a monocular vision navigation algorithm using optical flow with Principal Direction Screen Strategy is proposed. Firstly, we present an optical flow extraction and adjusting method based on Speed-up robust features (SURF), which makes the distribution of optical flow vectors more well-distributed and increases the accuracy of optical flow. Secondly, we constructive a complete ego-motion...
Nowadays, researches on accident prevention using train-mounted cameras had been progressing. Our proposed method considers temporal continuity between frames by using motion vectors in addition to conventional thresholding on similarity values obtained by a human detection method using HOG features. Experiments show the effectiveness of our method as compared with a previous method using only HOG...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
Current state of the art crowd density estimation methods are based on computationally expensive Gaussian process regression or Ridge regression models which can only handle a small number of features. In many computer vision applications, it has been empirically shown that a richer set of image features can lead to enhanced performances. In this paper, we reason that using more image features could...
In this paper, we propose a temporal stereo disparity estimation method. Conventional stereo disparity estimation methods rely on matching costs regarding computation of intensity or position similarities. However, most applications do not consider the temporal dimension when estimating the disparity. In other words, previous approaches disregard potentially useful disparity information that is already...
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle...
As the development of robot autonomous navigation, visual odometry gains more and more attention in the last few years. It can estimate the egomotion of a robot by analyzing the changes of the on-board camera view. This paper gives an overview of visual odometry and the development of its key technologies, such as feature detection, description and matching/tracking, camera pose estimation. The advantages...
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