Parking Space Detection System Using Video Images

2015 ◽  
Vol 2537 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Khaled Shaaban ◽  
Houweida Tounsi
Keyword(s):  
Detection System ◽  
Cost Savings ◽  
Weather Conditions ◽  
Video Camera ◽  
Parking Space ◽  
Image Histogram ◽  
Video Images ◽  
Fast Processing ◽  
Parking Space Detection ◽  
Novel Method

This study proposes a novel method for parking space detection. The proposed system is based on individual vehicle detection using grayscale images acquired from a video camera. Two algorithms were tested in the laboratory and the field. The first algorithm was based on the maximum value of the image histogram; the second algorithm was based on the bandwidth of the image histogram. The proposed algorithms successfully recognized vacant and occupied parking spaces under different scenarios and weather conditions. From the verification of the field study, the detection rate of the proposed system reached more than 98% for both algorithms. This system can be used for monitoring parking vacancy and guiding incoming motorists to vacant parking spaces in real time. The system has simple algorithms and easy configuration and does not require high-quality images. The latter feature means that less expensive cameras or existing surveillance cameras can easily be used instead of special cameras; thus huge cost savings are provided. The system also offers a fast processing time and easy applicability to parking lots in continuous operation.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

2019 ◽  
Vol 2673 (8) ◽  
pp. 221-232 ◽  
Author(s):  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Detection System ◽  
Weather Conditions ◽  
Video Camera ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Information

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

scholarly journals Implementation of an Available Parking Space Detection System in Hectic Parking Lots

2021 ◽  
Vol 11 (04) ◽  
pp. 688-701
Author(s):  
Diana Laura Gómez-Ruíz ◽  
Daphne Espejel-García ◽  
Graciela Ramírez-Alonso ◽  
Vanessa Verónica Espejel-García ◽  
Alejandro Villalobos-Aragón
Keyword(s):  
Detection System ◽  
Parking Space ◽  
Parking Lots ◽  
Parking Space Detection

3D Printing Conformal K-band Lens Antenna for a Smart Parking Space Detection System

2020 ◽  
pp. 1-1
Author(s):  
Zhanghua Cai ◽  
Yantao Zhou ◽  
Zibin Weng ◽  
Lei Deng ◽  
Yunlong Luo ◽  
...  
Keyword(s):  
3D Printing ◽  
Detection System ◽  
Parking Space ◽  
Lens Antenna ◽  
Parking Space Detection ◽  
Smart Parking ◽  
K Band

scholarly journals Nonlinear absorption and scattering of a single plasmonic nanostructure characterized by x-scan technique

2019 ◽  
Vol 10 ◽  
pp. 2182-2191 ◽  
Author(s):  
Tushar C Jagadale ◽  
Dhanya S Murali ◽  
Shi-Wei Chu
Keyword(s):  
Laser Scanning ◽  
Detection System ◽  
Nonlinear Behavior ◽  
Optical Nonlinearity ◽  
Research Area ◽  
Confocal Laser Scanning Microscope ◽  
Confocal Laser ◽  
Thin Sample ◽  
Channel Detection ◽  
Novel Method

Nonlinear nanoplasmonics is a largely unexplored research area that paves the way for many exciting applications, such as nanolasers, nanoantennas, and nanomodulators. In the field of nonlinear nanoplasmonics, it is highly desirable to characterize the nonlinearity of the optical absorption and scattering of single nanostructures. Currently, the common method to quantify optical nonlinearity is the z-scan technique, which yields real and imaginary parts of the permittivity by moving a thin sample with a laser beam. However, z-scan typically works with thin films, and thus acquires nonlinear responses from ensembles of nanostructures, not from single ones. In this work, we present an x-scan technique that is based on a confocal laser scanning microscope equipped with forward and backward detectors. The two-channel detection offers the simultaneous quantification for the nonlinear behavior of scattering, absorption and total attenuation by a single nanostructure. At low excitation intensities, both scattering and absorption responses are linear, thus confirming the linearity of the detection system. At high excitation intensities, we found that the nonlinear response can be derived directly from the point spread function of the x-scan images. Exceptionally large nonlinearities of both scattering and absorption are unraveled simultaneously for the first time. The present study not only provides a novel method for characterizing nonlinearity of a single nanostructure, but also reports surprisingly large plasmonic nonlinearities.

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