Congestion Control early warning system using Deep Learning

A new approach is proposed to analyze the live crowd and to provide an alert at the time of congestion, over-crowding and sudden gathering of pedestrians in a particular region. This paper proposes a completely software-oriented approach using MATLAB where it uses object detection and object tracking using Faster R- CNN (Region Based Convolutional Neural Network) algorithm where inception model of Google is used as CNN model which is pre-trained. This proposed method gives significant result on proposed dataset and the crowd congestion using Faster R-CNN approach which gives an accuracy of 93.503% at the rate 28 frames per second and the crowd detected video frames are uploaded to cloud storage

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

보행자 항법에서 계단 보행 시 진행 방향 신호의 형상 분석을 통한 층 결정 알고리즘

학위논문(석사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2022.2. 박찬국.This master’s thesis presents a new algorithm for determining floors in pedestrian navigation. In the proposed algorithm, the types of stairs are classified by shape analysis, and the floors are determined based on the stair type. In order to implement our algorithm, the walking direction estimated through the Pedestrian Dead Reckoning (PDR) system is used. The walking direction signal has different shapes depending on the stair types. Then, shape analysis is applied to the signal shapes of the walking direction to identify the types of stairs and determine the floor change. The proposed algorithm is verified through simulations and experiments, and it is confirmed that it works well even when moving through multiple floors with several different types of stairs. It is also verified that the performance is superior to the conventional floor determination algorithm.본 논문에서는 관성 측정 장치(IMU: Inertial Measurement Unit)를 이용한 실내 보행자 항법에서 계단을 통한 층 이동 시 계단의 종류를 파악하여 층을 결정하는 새로운 알고리즘을 제안한다. 이를 위해 보행자항법(PDR: Pedestrian Dead Reckoning) 시스템에서 추정된 고도, 걸음 검출 시간, 그리고 방향각을 사용한다. 이때 추정된 고도는 계단 보행을 시작하거나 마칠 때 평지 보행과 구분될 정도의 정확도만 필요하다. 따라서 본 알고리즘에서는 기존의 층 구분 알고리즘에서 필요로 하는 고도 추정치에 대한 의존성을 최소화할 수 있다. 제안한 알고리즘에서는 계단 보행 시에 나타나는 방향각의 신호에 통계적 형상 분석(statistical shape analysis) 기법을 적용하여 계단의 종류를 파악한 후 층을 구분하게 된다. 시뮬레이션과 실험을 통해 제안한 알고리즘의 정확도를 검증하며 여러 종류의 계단을 여러 층 오르내리는 경우에도 알고리즘이 잘 동작함을 확인한다. 그리고 기존의 층 구분 알고리즘에서 발생하는 시간 지연 문제를 해결하고 층 구분 정확도가 높아진 것을 확인한다. 또한 본 연구는 관성 측정장치 이외의 다른 센서나 무선통신 장치를 사용하지 않으며 층 높이와 같은 건물에 대한 기본 정보를 가정하지 않고도 층을 잘 결정할 수 있다는 유효성을 가진다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objectives and Contributions 2 Chapter 2 Pedestrian Dead Reckoning System 4 2.1 Overview of Pedestrian Dead Reckoning 4 2.2 Integration Approach 5 2.2.1 Strapdown inertial navigation system 5 2.2.2 Extended Kalman filter 6 2.2.3 INS-EKF-ZUPT 7 Chapter 3 Shape Analysis 10 3.1 Euclidean Similarity Transformation 11 3.2 Full Procrustes Distance 12 Chapter 4 Floor Determination 14 4.1 Stair Types 15 4.2 Stair Type Classification Algorithm 17 4.3 Floor Determination Algorithm 18 Chapter 5 Simulation and Experimental Results 22 5.1 Simulation Results 22 5.2 Experimental Results Single Floor Change 30 5.3 Experimental Results Multiple Floor Changes 32 5.3.1 Scenario 1 32 5.3.2 Scenario 2 37 Chapter 6 Conclusion 40 6.1 Conclusion and Summary 40 6.2 Future Work 41 Bibliography 42 국문초록 46석