scholarly journals Research Review on Parking Space Detection Method

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 128
Author(s):  
Yong Ma ◽  
Yangguo Liu ◽  
Lin Zhang ◽  
Yuanlong Cao ◽  
Shihui Guo ◽  
...  
Keyword(s):  
Detection Method ◽  
Urban Environments ◽  
Research Topic ◽  
Detection Methods ◽  
Research Review ◽  
Future Research ◽  
Parking Space ◽  
Reliable Detection ◽  
Parking Space Detection ◽  
Collision Avoidance System

The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research.

scholarly journals Early Detection of Vacant Parking Spaces Using Dashcam Videos

2019 ◽  
Vol 33 ◽  
pp. 9613-9618 ◽  
Author(s):  
Ming-Che Wu ◽  
Mei-Chen Yeh
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Detection Method ◽  
Metropolitan Areas ◽  
Point Of View ◽  
Detection Methods ◽  
Parking Space ◽  
Large Dataset ◽  
Parking Space Detection ◽  
Indoor And Outdoor

A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.

scholarly journals Parking Space Detection and Path Planning Based on VIDAR

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yi Xu ◽  
Shanshang Gao ◽  
Guoxin Jiang ◽  
Xiaotong Gong ◽  
Hongxue Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Detection Method ◽  
Parking Space ◽  
Planning Stage ◽  
Smooth Curves ◽  
Smooth Path ◽  
Detection Stage ◽  
Parking Space Detection ◽  
Automatic Parking ◽  
Simulation Results

The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.

scholarly journals A Comprehensive Study of Real-Time Vacant Parking Space Detection Towards the need of a Robust Model

2020 ◽  
Vol 19 (3) ◽  
pp. 99-106
Author(s):  
Rifath Mahmud ◽  
A. F. M. Saifuddin Saif ◽  
Dipta Gomes
Keyword(s):  
Performance Measures ◽  
Processing Time ◽  
Daily Basis ◽  
Detection Methods ◽  
Parking Space ◽  
Congested Traffic ◽  
Advantages And Disadvantages ◽  
Robust Model ◽  
Parking Space Detection ◽  
Comprehensive Study

Detection of vacant parking space is becoming a challenging task gradually. Space utilization and management of vehicle space is now a demandable field of research. Searching for an empty parking space in congested traffic is a time-consuming process. The existing vacant parking space detection methods are not robust or generalized for images captured from different camera viewpoints. Finding a proper parking space in a busy city is really a challenging issue and people are facing this problem on a daily basis. The main purpose of this research is to comprehensively discuss the previous researches of vacant parking space detection and compare them from different aspects. Methods used in previous researches are descriptively discussed along with their advantages and disadvantages. The frameworks of previous researches were compared on six generalized phases and the experimental results are compared in terms of dataset, accuracy, processing time and other performance measures.  This research also focuses on the challenges of vision-based vacant parking space detection which will contribute to future researches and researchers can work to overcome these challenges.

scholarly journals Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 464
Author(s):  
Upesh Nepal ◽  
Hossein Eslamiat
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Detection Method ◽  
Urban Environments ◽  
Aerial Image ◽  
Detection Methods ◽  
System Failure ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Spot Detection ◽  
Landing Spot

In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure. Particularly, we investigate different versions of the YOLO objection detection method and compare their performances for the specific application of detecting a safe landing location for a UAV that has suffered an in-flight failure. We compare the performance of YOLOv3, YOLOv4, and YOLOv5l while training them by a large aerial image dataset called DOTA in a Personal Computer (PC) and also a Companion Computer (CC). We plan to use the chosen algorithm on a CC that can be attached to a UAV, and the PC is used to verify the trends that we see between the algorithms on the CC. We confirm the feasibility of utilizing these algorithms for effective emergency landing spot detection and report their accuracy and speed for that specific application. Our investigation also shows that the YOLOv5l algorithm outperforms YOLOv4 and YOLOv3 in terms of accuracy of detection while maintaining a slightly slower inference speed.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3649
Author(s):  
Yosuke Tomita ◽  
Tomoki Iizuka ◽  
Koichi Irisawa ◽  
Shigeyuki Imura
Keyword(s):  
Gold Standard ◽  
Detection Method ◽  
Standard Measure ◽  
Detection Methods ◽  
Phase Identification ◽  
Foot Pressure ◽  
Pressure System ◽  
Speed Skating ◽  
Stance Time ◽  
Integrated Detection

Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating.

scholarly journals Using a Process Approach to Pandemic Planning: A Case Study

2021 ◽  
Vol 11 (9) ◽  
pp. 4121
Author(s):  
Hana Tomaskova ◽  
Erfan Babaee Tirkolaee
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Approach ◽  
Research Topic ◽  
Future Research ◽  
Critical Situation ◽  
Pandemic Planning ◽  
The Difference ◽  
Smart Technologies

The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic.

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