Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality--vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

실시간 자율주행 인지 시스템을 위한 신경 네트워크와 군집화 기반 미학습 물체 감지기 통합

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 이경수.최근 몇 년간, 센서 기술의 발전과 컴퓨터 공학 분야의 성과들로 인하여 자율주행 연구가 더욱 활발해지고 있다. 자율주행 시스템에 있어서 차량 주변 환경을 인식하는 것은 안전 및 신뢰성 있는 주행을 하기 위해 필요한 가장 중요한 기능이다. 자율주행 시스템은 크게 인지, 판단, 제어로 구성되어 있는데, 인지 모듈은 자율주행 차량이 경로를 설정하고 판단, 제어를 함에 앞서 주변 물체의 위치와 움직임을 파악해야하기 때문에 중요한 정보를 제공한다. 자율주행 인지 모듈은 주행 환경을 파악하기 위해 다양한 센서가 사용된다. 그 중에서도 LiDAR은 현재 많은 자율주행 연구에서 가장 널리 사용되는 센서 중 하나로, 물체의 거리 정보 획득에 있어서 매우 유용하다. 본 논문에서는 LiDAR에서 생성되는 포인트 클라우드 raw 데이터를 활용하여 장애물의 3D 정보를 파악하고 이들을 추적하는 인지 모듈을 제안한다. 인지 모듈의 전체 프레임워크는 크게 세 단계로 구성된다. 1단계는 비지면 포인트 추정을 위한 마스크 생성, 2단계는 특징 추출 및 장애물 감지, 3단계는 장애물 추적으로 구성된다. 현재 대부분의 신경망 기반의 물체 탐지기는 지도학습을 통해 학습된다. 그러나 지도학습 기반 장애물 탐지기는 학습한 장애물을 찾는다는 방법론적 한계를 지니고 있다. 그러나 실제 주행상황에서는 미처 학습하지 못한 물체를 마주하거나 심지어 학습한 물체도 놓칠 수 있다. 인지 모듈의 1단계에서 이러한 지도학습의 방법론적 한계에 대처하기 위해 포인트 클라우드를 일정한 간격으로 구성된 3D 복셀(voxel)로 분할하고, 이로부터 비접지점들을 추출한 뒤 미지의 물체(Unknown object)를 탐지한다. 2단계에서는 각 복셀의 특성을 추출 및 학습하고 네트워크를 학습시킴으로써 객체 감지기를 구성한다. 마지막 3단계에서는 칼만 필터와 헝가리안 알고리즘을 활용한 다중 객체 탐지기를 제안한다. 이렇게 구성된 인지 모듈은 비지면 점들을 추출하여 학습하지 않은 물체에 대해서도 미지의 물체(Unknown object)로 감지하여 실시간으로 장애물 탐지기를 보완한다. 최근 라이다를 활용한 자율주행 용 객체 탐지기에 대한 연구가 활발히 진행되고 있으나 대부분의 연구들은 단일 프레임의 물체 인식에 대해 집중하여 정확도를 올리는 데 집중하고 있다. 그러나 이러한 연구는 감지 중요도와 프레임 간의 감지 연속성 등에 대한 고려가 되어있지 않다는 한계점이 존재한다. 본 논문에서는 실시간 성능을 얻기 위해 이러한 부분을 고려한 성능 지수를 제안하고, 실차 실험을 통해 제안한 인지 모듈을 테스트, 제안한 성능 지수를 통해 평가하였다.In recent few years, the interest in automotive researches on autonomous driving system has been grown up due to advances in sensing technologies and computer science. In the development of autonomous driving system, knowledge about the subject vehicles surroundings is the most essential function for safe and reliable driving. When it comes to making decisions and planning driving scenarios, to know the location and movements of surrounding objects and to distinguish whether an object is a car or pedestrian give valuable information to the autonomous driving system. In the autonomous driving system, various sensors are used to understand the surrounding environment. Since LiDAR gives the distance information of surround objects, it has been the one of the most commonly used sensors in the development of perception system. Despite achievement of the deep neural network research field, its application and research trends on 3D object detection using LiDAR point cloud tend to pursue higher accuracy without considering a practical application. A deep neural-network-based perception module heavily depends on the training dataset, but it is impossible to cover all the possibilities and corner cases. To apply the perception module in actual driving, it needs to detect unknown objects and unlearned objects, which may face on the road. To cope with these problems, in this dissertation, a perception module using LiDAR point cloud is proposed, and its performance is validated via real vehicle test. The whole framework is composed of three stages : stage-1 for the ground estimation playing as a mask for point filtering which are considered as non-ground and stage-2 for feature extraction and object detection, and stage-3 for object tracking. In the first stage, to cope with the methodological limit of supervised learning that only finds learned object, we divide a point cloud into equally spaced 3D voxels the point cloud and extract non-ground points and cluster the points to detect unknown objects. In the second stage, the voxelization is utilized to learn the characteristics of point clouds organized in vertical columns. The trained network can distinguish the object through the extracted features from point clouds. In non-maximum suppression process, we sort the predictions according to IoU between prediction and polygon to select a prediction close to the actual heading angle of the object. The last stage presents a 3D multiple object tracking solution. Through Kalman filter, the learned and unlearned objects next movement is predicted and this prediction updated by measurement detection. Through this process, the proposed object detector complements the detector based on supervised learning by detecting the unlearned object as an unknown object through non-ground point extraction. Recent researches on object detection for autonomous driving have been actively conducted, but recent works tend to focus more on the recognition of the objects at every single frame and developing accurate system. To obtain a real-time performance, this paper focuses on more practical aspects by propose a performance index considering detection priority and detection continuity. The performance of the proposed algorithm has been investigated via real-time vehicle test.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Overview and Previous Researches 4 1.3. Thesis Objectives 12 1.4. Thesis Outline 14 Chapter 2 Overview of a Perception in Automated Driving 15 Chapter 3 Object Detector 18 3.1. Voxelization & Feature Extraction 22 3.2. Backbone Network 25 3.3. Detection Head & Loss Function Design 28 3.4. Loss Function Design 30 3.5. Data Augmentation 33 3.6. Post Process 39 Chapter 4 Non-Ground Point Clustering 42 4.1. Previous Researches for Ground Removal 44 4.2. Non-Ground Estimation using Voxelization 45 4.3. Non-ground Object Segmentation 50 4.3.1. Object Clustering 52 4.3.2. Bounding Polygon 55 Chapter 5 . Object Tracking 57 5.1. State Prediction and Update 58 5.2. Data Matching Association 60 Chapter 6 Test result for KITTI dataset 62 6.1. Quantitative Analysis 62 6.2. Qualitative Analysis 72 6.3. Additional Training 76 6.3.1. Additional data acquisition 78 6.3.2. Qualitative Analysis 81 Chapter 7 Performance Evaluation 85 7.1. Current Evaluation Metrics 85 7.2. Limitations of Evaluation Metrics 87 7.2.1. Detection Continuity 87 7.2.2. Detection Priority 89 7.3. Criteria for Performance Index 91 Chapter 8 Vehicle Tests based Performance Evaluation 95 8.1. Configuration of Vehicle Tests 95 8.2. Qualitative Analysis 100 8.3. Quantitative Analysis 105 Chapter 9 Conclusions and Future Works 107 Bibliography 109 국문 초록 114Docto

Positioning Commuters And Shoppers Through Sensing And Correlation

Positioning is a basic and important need in many scenarios of human daily activities. With position information, multifarious services could be vitalized to benefit all kinds of users, from individuals to organizations. Through positioning, people are able to obtain not only geo-location but also time related information. By aggregating position information from individuals, organizations could derive statistical knowledge about group behaviors, such as traffic, business, event, etc. Although enormous effort has been invested in positioning related academic and industrial work, there are still many holes to be filled. This dissertation proposes solutions to address the need of positioning in people’s daily life from two aspects: transportation and shopping. All the solutions are smart-device-based (e.g. smartphone, smartwatch), which could potentially benefit most users considering the prevalence of smart devices. In positioning relevant activities, the components and their movement information could be sensed by different entities from diverse perspectives. The mechanisms presented in this dissertation treat the information collected from one perspective as reference and match it against the data collected from other perspectives to acquire absolute or relative position, in spatial as well as temporal dimension. For transportation, both driver and passenger oriented solutions are proposed. To help drivers improve safety and ease the tension from driving, two correlated systems, OmniView [1] and DriverTalk [2], are provided. These systems infer the relative positions of the vehicles moving together by matching the appearance images of the vehicles seen by each other, which help drivers maintain safe distance from surrounding vehicles and also give them opportunities to precisely convey driving related messages to targeted peer drivers. To improve bus-riding experience for passengers of public transit systems, a system named RideSense [3] is developed. This system correlates the sensor traces collected by both passengers’ smart devices and reference devices in buses to position passengers’ bus-riding, spatially and temporally. With this system, passengers could be billed without any explicit interaction with conventional ticketing facilities in bus system, which makes the transportation system more efficient. For shopping activities, AutoLabel [4, 5] comes into play, which could position customers with regard to stores. AutoLabel constructs a mapping between WiFi vectors and semantic names of stores through correlating the text decorated inside stores with those on stores’ websites. Later, through WiFi scanning and a lookup in the mapping, customers’ smart devices could automatically recognize the semantic names of the stores they are in or nearby. Therefore, AutoLabel-enabled smart device serves as a bridge for the information flow between business owners and customers, which could benefit both sides

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms

Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally intensive tasks are assigned to one or more accelerators, such as GPUs and FPGAs. The refactoring of systems for execution on such platforms is highly desired but also difficult to perform, mainly due the inherent increase in software complexity. After exploration, we have identified a current need for a systematic approach that supports engineers in the refactoring process -- from CPU-centric applications to software that is executed on heterogeneous platforms. In this paper, we introduce a decision framework that assists engineers in the task of refactoring software to incorporate heterogeneous platforms. It covers the software engineering lifecycle through five steps, consisting of questions to be answered in order to successfully address aspects that are relevant for the refactoring procedure. We evaluate the feasibility of the framework in two ways. First, we capture the practitioner's impressions, concerns and suggestions through a questionnaire. Then, we conduct a case study showing the step-by-step application of the framework using a computer vision application in the automotive domain.Comment: Manuscript submitted to the Journal of Systems and Softwar