微波雷达辅助视频车辆分类检测器

车辆检测与分类系统是智能交通系统中的一个重要组成部分,其功能是检测路上车流量并对车辆进行分类,为道路监控和交通规划提供信息。本文提出了一种微波雷达辅助视频车辆检测与分类系统,微波雷达检测模块对经过的车辆进行检测,并触发视频分类模块结合雷达传感器信息对车辆进行分类。实验表明,该系统车辆检测率达到98％,车辆分类的准确率达到84％。     

基于激光与视觉的车辆防撞技术的研究

提出了一种基于视觉与激光测距相结合的车辆防撞技术.首先采用改进的Hough变换对车道标志线进行检测与提取,根据车辆底部的阴影特征搜寻车辆可能出现的位置;利用边缘检测排除非车辆区域;然后结合车辆在图像中的宽度,利用激光测距传感器对前方车辆动态扫描,计算出两车的车距和相对速度;最后用卡尔曼滤波算法连续跟踪检测到的车辆;实验表明:该方法能够实时有效地检测前方车辆,在高精度汽车防撞系统中具有一定的意义.     

基于视频的车辆检测与跟踪算法综述

首先介绍了交通检测系统,指出视频交通检测技术日益成为计算机视觉领域中备受关注的前沿方向。在此基础上,分别讨论了常用的车辆检测算法,基于模型的车辆检测算法,车辆跟踪的基本类型,以及基于模板匹配、卡尔曼滤波和粒子滤波的车辆跟踪算法,同时分析比较了各种算法的优缺点。最后,展望了这一领域未来研究的热点。     

基于车型特征提取的车辆检测程序的实现

车辆检测系统是智能交通系统的重要环节,基于视频图像的车型识别和车辆检测技术研究也越来越多。本文以实时视频为基础,介绍了一种车辆特征提取算法-弹性松弛袋算法,来提取车辆的车长车高特征,从而实现对车辆的检测和分类。本文着重介绍了弹性松弛袋算法提取车型特征的程序实现,通过实验的结果,验证了该算法的可行性。     

一种车辆超限自动检测系统设计

针对目前越来越严重的车辆超限现象,设计了一种能够自动检测超限车辆的系统,该系统具有不限制车辆正常行驶速度的动态称重设备,能够在车辆正常行驶的状态下对过往车辆进行检测,初步筛选出超限车辆;同时具有车辆牌照识别设备,通过数字摄像技术对车辆图像进行采集后,采用先进的图像处理技术,能够自动识别超限车辆的车牌号码;并能够对超限车辆的信息进行显示及处理,主要介绍系统的总体结构设计及关键的实现技术。     

基于深度学习的车辆分类检测

本文所要谈论的是如何识别车辆的分类以及检测,首先,我们阐述了车辆检测的发展意义及发展背景,其次,对其中涉及的深度学习理论基础以及几种经典的神经网络算法进行简洁的介绍。最后对车辆进行检测分类,并对每组数据的请准度做出统计,以便总结实验优缺点以及未来该系统的改进方法。     

交通监控视频中的车辆异常行为检测

构建了利用交通监控视频对车辆异常行为进行检测的系统框架.使用改进Surendra背景差分与三帧差分相结合的算法进行车辆目标检测,结合CamShift算法与Kalman滤波器进行车辆目标跟踪,提取车辆质心绘制运动轨迹,针对车辆运动方向判别、违章变道、调头等行为提出了检测方法.实验结果表明,提出的交通监控视频中的车辆异常行为检测系统具有较高的实时性与准确性,部署简易快速,维护成本低廉,可以满足当今智能交通系统日益增长的需求.     

基于YOLOv2的复杂场景下车辆目标检测

车辆目标检测是自动驾驶中的一个重要环节.针对复杂场景下的车辆目标检测模型检测速度慢,检测精度和召回率低等问题,以YOLOv2网络为基础,使用K-means算法对自制驾驶员视角下的车辆数据集中目标边框进行聚类,改进网络中卷积层的激活函数,加载预训练模型,多尺寸图像训练,最终得到改进的车辆目标检测模型.实验表明,相对于传统的车辆检测模型,本文方法可以在保证检测速度的情况下,尽可能多地检测出更多车辆目标且精度较高.最终在测试集上的mAP和recall达到了84.93％和83.07％,FPS达到了66.     

超声车辆检测的状态机算法

车辆检测现在主要应用到的有红外、地磁、视频和超声波等几种技术,它们各自都具有优缺点.文中分析并设计了一种基于HY-SRF05超声波模块的车辆检测算法：状态机检测算法.该算法能对检测车辆的阀值进行动态的更新,达到准确高效的检测效果.同时,该算法也适用于中、大型停车场的车辆诱导系统和公路上交通车流量的检测.     

基于邻接传感器及神经网络的车辆分类算法

为了提高车辆分类的性能,基于邻接传感器网络和BP神经网络提出一个有效的车辆分类算法MSVCA.在本算法中,使用成本相对低廉、灵敏度高的地磁传感器,采集车辆对地磁场的磁扰动特征信号,并根据邻接传感器网络本身的几何特性估计车辆长度,最后采用BP神经网络对车辆进行分类.神经网络的输入包括车辆长度、速度以及特征向量序列,输出为预定义的车辆类型.仿真及路面实验获得了93.61%的准确率.结果表明该算法提高了车辆分类的准确性,且具有较高的精度和顽健性.     

Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference System

The main objective of this work is to automatically detect moving vehicles on the road. Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier is adopted in this paper to classify moving vehicles on road. An input traffic video scenes are taken with vertical and horizontal positioned cameras. The proposed system contains six major steps such as preprocessing, vehicle detection, tracking, structural matching, feature extraction and classification. In this proposed method, preprocessing consists of color conversion and noise removal. Vehicle detection is performed by using background subtraction and Otsu thresholding algorithm. Kalman filter is used in the third step to track moving vehicles in successive frames. In the fourth step, Active Shape Modelling method is used to recover the 3D shape of the vehicle in order to find the boundaries of vehicle. In the fifth step, features of the detected vehicles are extracted by Harrish corner detector, log Gabor filter and these features are taken into account to classify the types of vehicle. Finally, ANFIS is proposed to classify the vehicles which is trained by updating the membership function. Experimentation results provides better accuracy rate and low mean error rate when compared with the state of art methods.     

Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads

This paper presents a driver assistance system for vehicle detection and inter-vehicle distance estimation using a single-lens video camera on urban/suburb roads. The task of vehicle detection on urban/suburb roads is more challenging due to their high scene complexity. In this work, the still area of frame inside the host vehicle is first removed using temporal differencing, followed by detecting vanishing point. Segmentation of road regions is then conducted using vanishing point and road’s edge lines. Shadow regions at the bottoms of vehicles verified using the HOG feature and an SVM classifier are utilized to detect vehicle positions. The distances between the host and its front vehicles are estimated based on the locations of detected vehicles and vanishing point. Experimental results show varied performance of vehicle detection with different scenes of urban/suburb roads and the detection rate can achieve up to 94.08%, indicating the feasibility of the proposed method.     

Quaternion attitude and disturbance observer mass estimation with low-cost sensors for industrial vehicles with trailers

Information about the vehicle mass and ground slope is important for many assistance functions in road and logistics vehicles. In intralogistics, safe speed limits for tractors depend on both slope and trailer mass.This work presents an estimation procedure for the attitude of a vehicle and the mass of a trailer attached to it. The estimator works in two steps. First, the attitude is estimated using an extended Kalman filter based on acceleration and angular rate measurements complemented by a single track vehicle model. In the second step, this information is used together with velocity and motor torque data to estimate the trailer mass and friction coefficient. A Kalman filter based disturbance observer for parameter estimation is compared to a recursive least squares identification. The mass estimation is extended by a structural break test to detect sudden mass changes when hitching and unhitching trailers.Multiple variants of the estimation scheme are implemented on an intralogistics vehicle. The performance of the proposed attitude and mass estimation solutions is demonstrated in comparison to state of the art reference algorithms in a large number of experiments. Compared to state of the art estimators, the proposed estimator yields a median 54 % reduction of pitch estimation RMSE and 40 % reduction of the mass estimation error. The structural break detection is able to detect all instances of hitching and unhitching of trailers with few false positives.     

昼夜转换场景中的车辆检测

在城市交通流量视频检测系统中,昼夜转换是必须面对的问题,在白天和黑夜的过渡期间,简单的使用白天算法或夜间算法检测效果较差。本文提出一种针对昼夜转换场景的车辆检测算法,该算法首先提取出背景图像,针对昼夜转换场景中光线昏暗、变化较快的特点,建立了一种能够快速跟踪背景变化的背景更新模型;然后采用背景差分的方法检测运动车辆。实验表明,本文算法能够很好的检测昼夜转换场景中的运动车辆。     

基于单片机和车辆检测器的车型分析技术研究

车辆检测器的种类很多,检测方式和主要工作原理也各不相同,但车辆检测器都是基于车辆通过或存在,使检测器中能量发生变化而产生车辆感应信号。阐述地磁式车辆检测器的工作原理的,报告了国内外车型分类技术应用现状。进一步论述系统的硬件结构框图、基本功能及软件程序流程图,认为其应该是国内最有前途、最可能采用的一种车型自动分类技术。     

Toward a deep CNN and RS-GOA framework for vehicle detection,traffic flow estimation,and optimal path selection from surveillance videos

Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi-scale dense nested deep CNN (MSDN-DCNN) and regional search grasshopper optimization algorithm (RS-GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre-processed, which includes frame conversion, redundancy removal, and image enhancement. The pre-processed frames are given as input to the MSDN-DCNN for multi-scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS-GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN-RS-GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.     

Monocular precrash vehicle detection: features and classifiers.

Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.     

一种新型多车道车流量检测算法

为了实时有效地检测道路路口车流量信息,并为交通控制和管理提供准确的交通流数据,提出了一种新型的车流量检测算法。通过利用现有的路面标记进行图像对称分割并计算图像灰度值方差,来判断有无车辆通过,进而实现车流量计算。仿真结果表明,该算法不仅简单,易于实现,而且检测准确率高,实时性好,能够有效地为智能交通灯控制提供信息数据。