A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.

A Real-Time Multi-Vehicle Tracking Framework in Intelligent Vehicular Networks

In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.

Online RGB-D person re-identification based on metric model update

Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.

SAM-drivenMAE pre-training and background-awaremeta-learning for unsupervised vehicle re-identification

Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification(Re-ID).Re-ID models suffer from varying degrees of background interference caused by continuous scene variations.The recently proposed segment anything model(SAM)has demonstrated exceptional performance in zero-shot segmentation tasks.The combination of SAM and vehicle Re-ID models can achieve efficient separation of vehicle identity and background information.This paper proposes a method that combines SAM-driven mask autoencoder(MAE)pre-training and backgroundaware meta-learning for unsupervised vehicle Re-ID.The method consists of three sub-modules.First,the segmentation capacity of SAM is utilized to separate the vehicle identity region from the background.SAM cannot be robustly employed in exceptional situations,such as those with ambiguity or occlusion.Thus,in vehicle Re-ID downstream tasks,a spatiallyconstrained vehicle background segmentation method is presented to obtain accurate background segmentation results.Second,SAM-driven MAE pre-training utilizes the aforementioned segmentation results to select patches belonging to the vehicle and to mask other patches,allowing MAE to learn identity-sensitive features in a self-supervised manner.Finally,we present a background-aware meta-learning method to fit varying degrees of background interference in different scenarios by combining different background region ratios.Our experiments demonstrate that the proposed method has state-of-the-art performance in reducing background interference variations.

一种基于FACE标准的飞行器管理系统软件架构

为了解决飞行器管理系统中应用FACE标准的符合性问题,提出一种平台特定服务段和输入输出服务段的功能划分方法.为了满足段间访问的实时性,采用输入输出服务的接口调用表技术建立一种低延迟的接口调用模型,并采用静态库端口通讯的方式实现传输服务.搭建了满足FACE标准接口的飞行管理系统原型系统,通过对原型系统端到端时延的实验,验证了该软件架构的有效性.

A vehicle re-identification algorithm based on multi-sensor correlation

Magnetic sensors can be applied in vehicle recognition.Most of the existing vehicle recognition algorithms use one sensor node to measure a vehicle’s signature.However,vehicle speed variation and environmental disturbances usually cause errors during such a process.In this paper we propose a method using multiple sensor nodes to accomplish vehicle recognition.Based on the matching result of one vehicle’s signature obtained by different nodes,this method determines vehicle status and corrects signature segmentation.The co-relationship between signatures is also obtained,and the time offset is corrected by such a co-relationship.The corrected signatures are fused via maximum likelihood estimation,so as to obtain more accurate vehicle signatures.Examples show that the proposed algorithm can provide input parameters with higher accuracy.It improves the average accuracy of vehicle recognition from 94.0%to 96.1%,and especially the bus recognition accuracy from 77.6%to 92.8%.

基于改进的YOLOv3和Facenet的无人机影像人脸识别

基于无人机影像的高精度人脸识别在应急救援、嫌疑人员跟踪等场景中发挥着重要作用。深度学习卷积神经网络以其较高的精度和较少的人为干扰被广泛应用于目标检测识别领域,能很好地应用于无人机影像人脸识别任务中。探究在无人机嫌疑人员识别应用场景下利用卷积网络进行人脸高精度识别,用改进后的YOLOv3(you only look once)进行无人机影像的人脸检测,将得到的预测框对齐后输入到经典的Facenet人脸识别网络中进行目标身份的判定。实验对比了改进后的YOLOv3、原始YOLOv3和MTCNN(multi-task convolutional neural network)的检测效果以及结合Facenet进行人脸识别的效果。结果表明:1)改进后的YOLOv3相对于原始YOLOv3不仅精度和召回率得到提升,而且模型参数量有所减少,无人机影像的漏检和错检现象也轻于原始YOLOv3;此外,改进后的YOLOv3相对MTCNN的AP(average precision)提升9.49%,检测速度也约是MTCNN的3倍;2)改进后的YOLOv3+Facenet相对于原始YOLOv3+Facenet及MTCNN+Facenet对人脸的区分能力更强,精度更高,对遮挡以及模糊的鲁棒性也更强。

Lightweight Method for Vehicle Re-identification Using Reranking Algorithm Based on Topology Information of Surveillance Network

As an emerging visual task,vehicle re-identification refers to the identification of the same vehicle across multiple cameras.Herein,we propose a novel vehicle re-identification method that uses an improved ResNet-50 architecture and utilizes the topology information of a surveillance network to rerank the final results.In the training stage,we apply several data augmentation approaches to expand our training data and increase their diversity in a cost-effective manner.We reform the original RestNet-50 architecture by adding non-local blocks to implement the attention mechanism and replacing part of the batch normalization operations with instance batch normalization.After obtaining preliminary results from the proposed model,we use the reranking algorithm,whose core function is to improve the similarity scores of all images on the most likely path that the vehicle tends to appear to optimize the final results.Compared with most existing state-of-the-art methods,our method is lighter,requires less data annotation,and offers competitive performance.

Occlusion Based Discriminative Feature Mining for Vehicle Re-identification

Existing methods of vehicle re-identification(ReID)focus on training robust models on the fixed data while ignore the diversity in the training data,which limits generalization ability of the models.In this paper,it proposes an occlusion based discriminative feature mining(ODFM)method for vehicle re-identification,which increases the diversity of the training set by synthesizing occlusion samples,to simulate the occlusion problem in the real scene.To better train the ReID model on the data with large occlusions,an attention mechanism was introduced in the mainstream network to learn the discriminative features for vehicle images.Experimental results on two public ReID datasets,VeRi-776 and VehicleID verify the effectiveness of the proposed method comparing to the state-of-the-art methods.

面齿轮车齿展成建模及啮合性能分析

面齿轮因具有紧凑结构、高传动效率和高承重性等特点而具有巨大的发展前景.使用车齿法加工面齿轮相较于插齿、滚齿具有高效、低成本等优点.论文以正交直齿面齿轮为研究对象,对面齿轮车加工原理进行分析,并对在不同安装误差下的面齿轮轮齿进行接触分析(TCA),利用数值仿真方法分析偏置误差、轴交角误差和安装距误差对面齿轮接触轨迹的影响.使用YK2260MC数控车齿机床进行车齿加工,使用格里森650GMS齿轮检测齿面误差,结果显示,左侧齿面误差最大,为13.9μm,右侧齿面误差最大,为11.7μm.该结果验证了车齿加工数学模型的正确性,提高面齿轮的加工精度和效率,为进一步改进面齿轮车齿工艺提供了参考.

面向智能驾驶的人脸检测研究进展

智能驾驶作为汽车领域的一项前沿技术,日益受到广泛关注。人脸检测在智能驾驶中扮演着重要的角色,通过实时监测驾驶员和乘客的脸部特征能够提高车辆内部环境感知能力,增强系统对驾驶员和乘客的理解,从而提升交通安全性、舒适性和个性化服务体验。文章首先回顾了基于传统特征提取的人脸检测算法,然后介绍了基于深度学习的几种主流检测方法,包括基于级联卷积网络、单阶段检测及双阶段检测算法,分析了这几种算法的结构和优缺点并介绍了轻量级检测方法。最后,对未来的研究方向进行了展望。

煤矿双巷连掘工作面智能化锚杆钻车研究与应用

基于现有四臂顶锚杆钻车、六臂帮锚杆顶锚索钻车,对其进行智能化升级改造,研制智能化四臂顶锚杆钻车、智能化六臂帮锚杆顶锚索钻车,并在双巷连掘示范工作面开展试验应用。结果表明:2种钻车实现了顶/帮锚杆、顶锚索支护钻孔施工的电液智能化控制,在局部少量人工辅助下完成顶/帮锚杆支护钻孔、锚固全流程自动化施工,起到了减员、提效作用,为煤炭行业智能化转型提供技术支撑和有益参考。

某商用车的吹面风管设计优化

本文描述了某商用车的吹面风管整车边界以及吹面风窗位置确定、吹面风管设计的关系矩阵及设计细则等吹面风管设计内容,并通过对吹面风管内流场及出风情况的CFD分析,对吹面风管进行设计优化。

无轨胶轮车关键尺寸的设计及车架的有限元分析

为进一步提升无轨胶轮车在煤矿综采工作面巷道的生产效率和安全性,结合实践生产提出了无轨胶轮车的设计目标与相关要求,重点设计其通过性参数。基于相关参数,采用ANSYS软件对车架进行有限元仿真分析,重点验证了与其配套车架在转弯工况下的最大应力和应变,验证了通过性参数与车架的匹配性。

Chinese Mini Vehicle Makers Showing Confidence to Face up to Challenge

On November 19, 1999, a seminar on developing mini vehicle market was held in Beijing, with almost all major Chinese mini vehi-cle makers participating in. They are Chana Automobile (Group) Li-ability Corp., Ltd., Changhe Air-craft Industries (Group), Ltd., Harbin Dong’An Engine Manufac-turing Company, Harbin Aircraft Manufacturing Corp. and Liuzhou Wuling Auto Co., Ltd.. The boss-es of the five enterprises

车脸定位及识别方法研究

针对车牌无法识别的车辆,研究了一种车脸定位及识别方法。该方法分为两个阶段:首先,使用Adaboost算法进行车脸定位,并利用经验矩形方法进行定位改进;其次,在定位出来的车脸区域提取SIFT(scale-invariantfeature transform)和SURF(speeded up robust feature)局部不变性特征,利用这两种不变性特征的叠加及位置约束改进匹配算法,与标准车型数据库中的车脸特征进行匹配,根据匹配结果进行车脸识别,从而得到车辆类型。实验结果表明,该方法的正确识别率达到83.6%。交通卡口抓拍到的车辆照片基本是正前照,无法获取车身侧面信息分析其车型。针对车牌无法识别的车辆,通过车脸定位、特征提取,并与标准车型库中车脸进行对比,进而识别车脸,该识别车脸的方法为识别车型提供了一种新途径。

电动汽车前向仿真中驾驶员模型建模与仿真

介绍了在电动汽车前向仿真中所采用的驾驶员模型的建模方法 .针对仿真车速与需求车速之间的偏差使用 PI控制器进行修正而又难以整定 P,I系数的问题 ,提出了采用模糊控制器对车速偏差进行修正的方法 .按此方法对驾驶员模型进行建模 ,在电动汽车仿真软件 HEVSim中进行仿真测试 ,在实现仿真要求的前提下 ,仿真车速对需求车速的跟踪情况优于 PI控制器 .该驾驶员模型具有较好的鲁棒性 。

基于车前脸HOG特征的车型识别方法研究与实现

针对道路视频监控中车型识别的问题,为准确定位车前脸,提出了一种基于车前脸梯度方向直方图的识别算法。用形态学粗定位和投影细定位算法提取视频中车前脸区域,准确定位车前脸,能提高全局特征算法的识别效果。将车前脸图像的梯度方向直方图特征作为识别初始特征,采用线性判别分析算法进行特征提取,降低特征维数,提高识别速度。基于集成学习的思想,对车前脸进行网格分割,各子区域训练得到的分类器生成集成分类器,提高车型识别率。建立了15种车系80种车型的车前脸图像库进行实验,实验结果表明,上述方法的车型正确识别率为93.5%。

多特征融合的随机森林疲劳驾驶识别算法

复杂的交通环境、个人和社会因素制约了疲劳驾驶识别技术的应用效果,提出一种对视频中驾驶员脸部状态和车辆驾驶状态数据进行融合分析的疲劳驾驶识别算法。该算法基于Dlib库提取的人脸轮廓点计算眼和嘴的纵横比值,生成眯眼和哈欠特征,基于线性拟合趋势提取法生成车辆操控活跃度特征,然后采用改进后的随机森林模型对疲劳状态进行识别。该模型基于权重对特征的重要性进行评估,提高了树节点分裂的有效性,并给出了森林中树的数量的调控方法。实验结果表明所提算法的疲劳驾驶识别准确率均值达到了92.06%,并具有较好的计算效率,验证了其有效性。

基于YOLO的四旋翼无人机人脸识别实验平台

该文简要介绍了YOLO(you only look once)人脸识别算法的基本工作原理,设计了一套基于YOLO的四旋翼无人机人脸识别实验平台。该实验平台主要包括特洛(Tello)四旋翼无人机和地面站两个模块。地面站通过Wi-Fi模块与无人机通信来控制其飞行和图像采集,接收来自无人机采集的图像信息,并运行基于YOLO的人脸识别算法,对图像信息进行人脸检测与识别。该人脸识别实验平台涵盖了无人机飞行控制、无线通信、图像处理以及深度学习算法等内容,有助于学生深入学习和理解无人机控制和图像识别的原理及应用,能够培养和提高学生针对复杂工程问题的创新和工程实践能力。