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This paper presents an object detection accelerator that features many-scale (17), many-object (up to 50), multi-class (e.g., face, traffic sign), and high accuracy (average precision of 0.79/0.65 for AFW/BTSD datasets). Employing 10 gradient/color channels, integral features are extracted, and the results of 2,000 simple classifiers for rigid boosted templates are adaptively combined to make a strong...
In order to solve the defects of ViBe moving object detection algorithm, an improved algorithm based on spatial-temporal gradient has been proposed. According to the complexity and the changes of the background, parameters of discrimination mechanism have been adjusted adaptively. The mean of spatial gradients has been calculated to adjust the detection radius, which is capable of improving the detection...
Figure detection, separation and image classification are common problems occurring in various fields, especially medicine. Since image databases are usually large, manual classification would be a demanding task. In this paper, we proposed a method for automatic compound figure detection and separation, and gave a comparison between other recognition methods, such as convolutional neural networks...
Performance of high resolution image process is one of the kernel problems that must be addressed to promote the development of embedded system. In this study, a scalable bi-level parallel object detection framework based on heterogeneous manycore cluster was established to improve object detection performance for embedded device. First, the fundamental principle of local binary pattern and cascade...
Objective: Using the target classification is too complex be used to distinguish between natural objects and Man-made objects in aerial image, so in this paper, we analyze the target classification and feature of the target in the sea instead. According to the features of the contour map, a method for eliminating natural objects is proposed. Methods: First, we decompose the contour map through perspective...
Object detection is at the heart of nearly all the computer vision systems. Standard off-the-shelf embedded processors are hard to meet the trade-offs among performance, power consumption and flexibility for future algorithms required by object detection applications. Therefore, this paper presents an Application Specific Instruction set Processor (ASIP) for object detection by using AdaBoost-based...
MOOCs have refined the way of teaching where the electronic devices can provide knowledge on the go, which says that knowledge is omnipresent. The connectivism is a technique where the knowledge is contained in the electronic media, which can be delivered as per the requirement. The use of Face detection with Haar based algorithms help to track the current status of the user and perform the teaching...
A detection method for cheating behavior in examination room based on artificial bee colony algorithm is presented. The problem of moving objects detection is transformed into the difference function of color value between foreground and background. Artificial bee colony algorithm is applied for optimizing the objective function. The background component is separated from the sequence images by value...
There are two main approaches to hyperspectral target detection: anomaly detection techniques, which detect outliers substantially different from the background, and spectral signature techniques, which require as an input a user-defined target signature. Oftentimes, however, the target signature may not be known, or there may be unexpected targets in the image, which are unknown but still of interest...
The article is devoted to new approaches in accuracy evaluation of information retrieval systems (with tests made for motion detection algorithms). First part of article is devoted to studying of how good is tested motion detection algorithm. New elements are added to ROC for computing new measures. These elements are based on Equal Error Rate criterion. Second part of article is devoted to visualization...
In wireless network, the resource limitations and process speed are the main constraints. They make many traditional background subtraction methods unable to be applied in wireless network. Vibe algorithm is a newly proposed background subtraction technique and its computational load is very low. That makes it available in the wireless network. However, Vibe is a pixel-level algorithm and it does...
Recently, An interest about the unmanned vehicles is increasing, and a related research has been actively researched. Application systems using the partial element of technologies are commercialized. The information about surrounding environment should be able to use effectively in order to perform a given task such as robot navigation, path planning, and obstacle avoidance. The essential function...
The purpose of this paper is to propose a method to abstract and classify vehicle data collected from vision sensors into road scenarios. The classified scenarios can be played back on specialized hardware designed to handle these scenarios to characterize its performance. Since the majority of existing automotive computer vision systems mandate real-time results, this study aims to introduce the...
We discuss FPGA implementations of object (such as face) detectors in video streams using the accurate Haar-feature based algorithm. Rather than creating one implementation for one FPGA, we develop a method to generate a series of implementations that have different size and performance to target different FPGA devices. The automatic generation was enabled by custom design space exploration on a particular...
This paper proposes an efficient scheme for detecting different object classes in an imaging sensor network. The object detection system detects all the instances of objects (for which the classifier was trained) in the given image, regardless of their scales and locations. Therefore, the image can be thus seen as a set of sub-windows that are to be evaluated by the detector. The detector selects...
Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
A semen analysis evaluates certain characteristics of a male's semen and the sperm contained in the semen. The chance of pregnancy will be reduced, if more than 50 percent of a man's sperm lack movement. Assessing the ability of sperm to move forward through the cervix into the fallopian tubes is a widely used measure of male infertility. Since the examination is done by a person through a microscope,...
This paper presents a real-time algorithm for detecting and tracking bicyclists or pedestrians using a laser device. By processing the sequence of the range images, the algorithm outputs trajectory and speed of each object during the period when he is in the detection region. The whole algorithm consists of two parts, which are the object detection and the object tracking. In the former, the multi-level...
The detection of broken railway fastener is important to ensure the safety of the railway transport. This paper proposes an efficient method to detect and recognize the broken fastener with complex ballast railway images. Firstly, a from-coarse-to-fine strategy according to the sleeper region's gray and gradient characteristics is used to position the fastener, then the Haar-like feature set according...
AdaBoost has proved to be an effective method to improve the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost might suffer from the overfitting problem, especially for noisy data. In addition, it still needs much time to train the classifier using AdaBoost. In this paper, we focus on designing an algorithm named Heritance AdaBoostRF...
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