Computer vision techniques for traffic flow computation

This paper describes an application of computer vision techniques to road surveillance. It reports on a project undertaken in collaboration with the Research and Innovation group at the Ordnance Survey. The project aims to produce a system that detects and tracks vehicles in real traffic scenes to generate meaningful parameters for use in traffic management. The system has now been implemented using two different approaches: a feature-based approach that detects and groups corner features in a scene into potential vehicle objects, and an appearance-based approach that trains a cascade of classifiers to learn the appearances of vehicles as an arrangement of a set of pre-defined simple Haar features. Potential vehicles detected are then tracked through an image sequence, using the Kalman filter motion tracker. Experimental results of the algorithms are presented in this paper.     

Computer vision techniques for construction safety and health monitoring

For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups—object detection, object tracking, and action recognition—based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques.     

Computer vision for artists and designers: pedagogic tools and techniques for novice programmers

This article attempts to demystify computer vision for novice programmers through a survey of new applications in the arts, system design considerations, and contemporary tools. It introduces the concept and gives a brief history of computer vision within interactive art from Myron Kruger to the present. Basic techniques of computer vision such as detecting motion and object tracking are discussed in addition to various software applications created for exploring the topic. As an example, the results of a one-week machine vision workshop are presented to show how designers are able to apply their skills toward creating novel uses of these technologies. The article concludes with a listing of code for basic computer vision techniques.     

Computer vision system for applications in robotics education

Computer vision systems based on Vidicon or charge-coupled device cameras are relatively expensive. A very economical computer vision system has been developed for undergraduate teaching and project work in robotics. The system comprises a modified dynamic RAM circuit, acting as the light-imaging component, interfaced to an Aim 65 microcomputer.     

Computer vision for behaviour-based safety in construction: A review and future directions

The process of identifying and bringing to the fore people’s unsafe behaviour is a core function of implementing a behaviour-based safety (BBS) program in construction. This can be a labour-intensive and challenging process but is needed to enable people to reflect and learn about how their unsafe actions can jeopardise not only their safety but that of their co-workers. With advances being made in computer vision, the capability exists to automatically capture and identify unsafe behaviour and hazards in real-time from two-dimensional (2D) digital images/videos. The corollary developments in computer vision have stimulated a wealth of research in construction to examine its potential application to practice. Hindering the application of computer vision in construction has been its inability to accurately, and generalise the detection of objects. To address this shortcoming, developments in deep learning have provided computer vision with the ability to improve the accuracy, reliability and ability to generalise object detection and therefore its usage in construction. In this paper we review the developments of computer vision studies that have been used to identify unsafe behaviour from 2D images that arises on construction sites. Then, in light of advances made with deep learning, we examine and discuss its integration with computer vision to support BBS. We also suggest that future computer-vision research should aim to support BBS by being able to: (1) observe and record unsafe behaviour; (2) understand why people act unsafe behaviour; (3) learn from unsafe behaviour; and (4) predict unsafe behaviour.     

基于机器视觉的快速车道线识别

为了克服已有车道线识别算法运算复杂、速度较慢以及鲁棒性欠缺等不足之处, 提出一种新的快速车道线识别算法, 首先通过对图像的灰度变化分析, 得出车道线轮廓像素, 然后运用B-Spline曲线拟合车道线轮廓, 得到最终的识别效果图。实验表明, 该算法在速度和识别率上都能取得优异的表现。在嵌入式平台上, 该算法取得了12 fps的速度, 符合智能驾驶的实际需求。     

一种改进的基于机器视觉的边缘检测算法

图像的边缘保留了图像最基本也是最重要的特征，边缘检测减少了数据量，在机器视觉系统中得到广泛的应用，是图形图像领域里的重要研究方向之一．本文提出了一种基于小波变换技术和数学形态学的边缘检测方法，并用智能图像传感器获得的图像教据对所提出的方法进行了验证．实验结果表明：该方法可较好地提取视觉图像的边缘信息．     

Intelligent vision system for dynamic environments

This article describes an intelligent vision system that absorbs useful information from its environment and draws useful conclusions. This system can give instructions to locate vacant seats that are currently occupied in a theater. The extraction of useful information without viewing or exposing the inside details of an environment through an active vision system is proposed. Reasoning-based conclusions are drawn for optimum searching. The effectiveness of the proposed method is demonstrated using an experiment. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

A survey of Optimal Transport for Computer Graphics and Computer Vision

Optimal transport is a long-standing theory that has been studied in depth from both theoretical and numerical point of views. Starting from the 50s this theory has also found a lot of applications in operational research. Over the last 30 years it has spread to computer vision and computer graphics and is now becoming hard to ignore. Still, its mathematical complexity can make it difficult to comprehend, and as such, computer vision and computer graphics researchers may find it hard to follow recent developments in their field related to optimal transport. This survey first briefly introduces the theory of optimal transport in layman's terms as well as most common numerical techniques to solve it. More importantly, it presents applications of these numerical techniques to solve various computer graphics and vision related problems. This involves applications ranging from image processing, geometry processing, rendering, fluid simulation, to computational optics, and many more. It is aimed at computer graphics researchers desiring to follow optimal transport research in their field as well as optimal transport researchers willing to find applications for their numerical algorithms.     

基于机器视觉的煤岩界面识别研究

针对现有煤岩识别方法存在煤岩界面传感器结构复杂、可靠性差、普适性差等问题,提出了一种基于机器视觉的煤岩界面识别系统设计方案,给出了系统总体结构,分析了系统识别煤岩界面的工作原理,重点讨论了图像特征选取和分类器的设计。该系统根据灰度共生矩阵理论提取煤岩图像的22种纹理特征,采用增l减r法搜索出优选特征,最后运用线性函数判别法构建煤岩分类器模型。实验结果表明,该系统的煤岩分类器模型性能稳定,具有较强的识别能力。     

The role of computer vision in prosthetic vision

The cost of vision loss worldwide has been estimated at nearly $3 trillion (http://www.amdalliance.org/cost-of-blindness.html). Non-preventable diseases cause a significant proportion of blindness in developed nations and will become more prevalent as people live longer. Prosthetic vision technologies including retinal implants will play an important therapeutic role. Retinal implants convert an input image stream to visual percepts via stimulation of the retina. This paper highlights some barriers to restoring functional human vision for current generation visual prosthetic devices that computer vision can help overcome. Such computer vision is interactive, aiming to restore function including visuo-motor tasks and recognition.     

Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction

Predicting unsafe behaviour in advance can enable remedial measures to be put in place to mitigate likely accidents on construction sites. Prevailing safety studies in construction tend to be retrospective and focus on examining the conditions that contribute to unsafe behaviour from a psychological perspective. While such studies are warranted, they can also not visually comprehend the dynamic and complex conditions that influence unsafe behaviour. In this paper, we aim to contribute to filling this void and, in doing so, combine computer vision with Long-Short Term Memory (LSTM) to predict unsafe behaviours from videos automatically. Our proposed approach for predicting unsafe behaviour is based on: (1) tracking people using a SiamMask; (2) predicting the trajectory of people using an improved Social-LSTM; and (3) predicting unsafe behaviour using Franklin's point inclusion polygon (PNPoly) algorithm. We use the Wuhan metro project as a case to evaluate our approach’s feasibility and effectiveness. Our adopted SiamMask method outperforms current techniques used for tracking people. Additionally, our improved Social-LSTM can achieve higher accuracy on trajectory prediction than other methods (e.g., Social-GAN). The research findings demonstrate that our developed computer vision approach can be used to accurately predict unsafe behaviour on construction sites.     

基于计算机视觉的钢轨扣件螺母缺失检测系统

针对传统方法己经不能满足线路检修的需要,设计了一种基于计算机视觉的钢轨扣件螺母缺失检测系统,建立了钢轨扣件螺母缺失检测系统的系统框架,提出了一种基于特定区域像素点扫描统计的扣件定位算法,使用了主元分析法算法来提取扣件螺母特征向量,并利用最小距离分类器对扣件进行分类。实验结果表明,该系统能有效地自动识别钢轨扣件螺母是否缺失,能在一定程度上代替扣件系统螺母的人工巡检。     

基于计算机视觉的光电误差参数自动标校方法

针对传统人工方法进行雷达光电误差参数标校精度低、耗时长等问题,提出了一种基于计算机视觉的方法,依次通过局部色彩均值向量匹配、非极大值抑制（ NMS）聚类、方向梯度匹配的方法实现光标的自动定位。实验表明,该方法与脉冲耦合神经网络（ PCNN）方法相比鲁棒性强,具有较高的识别准确率;与人工标校方法相比,提高了标校精度并减少了耗时。     

Robocon2007移动机器人视觉系统

根据Robocon2007的比赛规则,设计出了一套适用于该比赛的机器视觉系统。系统基于通用PC机,利用一个通用摄像机采集彩色图像,系统构造简单且经济。视觉系统的软件实现了比赛场地导引线的分割、得分点的识别以及机器人与目标的相对定位等功能。在导引线分割中提出了双阈值p-tile自适应算法,提高了分割算法对环境光变化的适应性;得分点位的识别中设计了利用导引线交点与目标颜色相结合的综合定位策略,有效解决了目标点位与地面颜色近似,无法直接从单目图像识别的难题。在2007年度的Robocon比赛中,该机器视觉系统表现出了很强的环境适应性和较高的自定位精度,为国防科大队获得最佳策略奖奠定了基础。     

Context aided pedestrian detection for danger estimation based on laser scanner and computer vision

Road safety applications demand the most reliable sensor systems. In recent years, the advances in information technologies have led to more complex road safety applications able to cope with a high variety of situations. These applications have strong sensing requirements that a single sensor, with the available technology, cannot attain. Recent researches in Intelligent Transport Systems (ITS) try to overcome the limitations of the sensors by combining them. But not only sensor information is crucial to give a good and robust representation of the road environment; context information has a key role for reliable safety applications to provide reliable detection and complete situation assessment. This paper presents a novel approach for pedestrian detection using sensor fusion of laser scanner and computer vision. The application also takes advantage of context information, providing danger estimation for the pedestrians detected. Closing the loop, the danger estimation is later used, together with context information, as feedback to enhance the pedestrian detection process.     

MinGPU: a minimum GPU library for computer vision

In the field of computer vision, it is becoming increasingly popular to implement algorithms, in sections or in their entirety, on a graphics processing unit (GPU). This is due to the superior speed GPUs offer compared to CPUs. In this paper, we present a GPU library, MinGPU, which contains all of the necessary functions to convert an existing CPU code to GPU. We have created GPU implementations of several well known computer vision algorithms, including the homography transformation between two 3D views. We provide timing charts and show that our MinGPU implementation of homography transformations performs approximately 600 times faster than its C++ CPU implementation.

The Imalab method for vision systems

We propose a method of constructing computer vision systems using a workbench based on a rich extensible toolbox and a general-purpose kernel. The toolbox provides access to an open set of libraries; the kernel provides incremental dynamic system construction and interactivity. This method makes it possible to quickly develop and test new algorithms, simplifies the use and reuse of existing program libraries, and allows to construct a variety of systems to meet particular requirements. Major strong points of our approach are: (1) Imalab is a single environment for different types of users who share the same basic code with different interfaces and tools. (2) New library modules are added quickly and easily, including libraries for scientific domains other than vision (e.g., robotics, Bayesian reasoning, automatic learning). (3) Different programming languages - C/C + + and several symbolic languages (Lisp, Prolog, Clips) - are tied together in a single system. We consider this an important advantage for the implementation of cognitive vision functionalities. (4) Automatic program generation simplifies the integration of libraries and makes the multilanguage feature work smoothly. (5) Efficiency: library code runs without overhead. The Imalab system has been in use for several years now, and we have started to distribute it.Published online: 4 November 2004This research is partially funded by the European Commissions IST project DETECT (IST-2001-32157)     

Modular architecture for custom-built systems oriented to real-time computer vision: Application to color recognition

This paper presents a modular architecture called DIPSA, which is intended to be used for building custom-made real-time Computer-Vision systems. It consists of four module types and each of them represents a family of circuits that perform specific visual tasks. Our architectural model proposes an algorithm-dependent methodology and makes good results possible using problem oriented solutions. The desired performance is achieved choosing the appropriate modules and connecting them by means of heterogeneous pipeline and concurrence. Additionally, two DIPSA-based hardware systems for real-time Color Recognition are described here.