Detection of parking slots occupation by temporal difference of inverse perspective mapping from vehicle-borne monocular camera

2021 ◽  
pp. 095440702110055
Author(s):  
Zhaozheng Hu ◽  
Hanbiao Xiao ◽  
Zhe Zhou ◽  
Na Li
Keyword(s):  
Low Cost ◽  
Ground Plane ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
Temporal Difference ◽  
Difference Map ◽  
Monocular Camera ◽  
Automatic Parking ◽  
The Difference ◽  
Inverse Perspective Mapping

Detection of parking slots occupation is a crucial task for parking assistance, automatic parking, and autonomous driving systems. This paper proposed a novel method, called Temporal Difference of Inverse Perspective Mapping Difference (TD-IPM), without explicit 3D reconstruction or objection detection. In this method, temporal images from monocular camera are first inverse perspective mapped (IPM) onto the ground plane based on camera calibration results. Second, we proposed an algorithm, called Block Consensus based on Rotation Invariance Phase-Only Correlation (BC-RIPOC), for fast and robust motion estimation. From the estimated motion, we can align these two IPM images and generate IPM difference map. Third, the IPM difference map is segmented and filtered to generate a binary map that can distinguish objects on the ground plane or not for occupation detection. The obstacle is readily localized from the difference map as well. The proposed TD-IPM method has been validated in both underground and outdoor parking lots. Experimental results demonstrate that the proposed TD-IPM method can successfully detect various occupation objects, such as vehicles, cones, lockers, and others, with 97.9% average detection accuracy and speed of 17.5 frames per second (fps). The proposed method suggests an effective and low-cost solution to intelligent parking systems.

scholarly journals Smoke recognition network based on dynamic characteristics

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092566
Author(s):  
Dahan Wang ◽  
Sheng Luo ◽  
Li Zhao ◽  
Xiaoming Pan ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
State Of The Art ◽  
The State ◽  
Detection Accuracy ◽  
Static Characteristics ◽  
Good Tool ◽  
Early Signal ◽  
Fuzzy Objects ◽  
The Difference ◽  
Smoke Recognition

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

Reducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations

2020 ◽  
Vol 14 (2) ◽  
pp. 167-175
Author(s):  
Li Zhang ◽  
Volker Schwieger
Keyword(s):  
Low Cost ◽  
Ground Plane ◽  
Single Frequency ◽  
Spatial Correlations ◽  
Raw Data ◽  
Multipath Effect ◽  
Temporal Correlations ◽  
Multipath Effects ◽  
Gnss Receivers ◽  
Multipath Signals

AbstractThe investigations on low-cost single frequency GNSS receivers at the Institute of Engineering Geodesy (IIGS) show that u-blox GNSS receivers combined with low-cost antennas and self-constructed L1-optimized choke rings can reach an accuracy which almost meets the requirements of geodetic applications (see Zhang and Schwieger [25]). However, the quality (accuracy and reliability) of low-cost GNSS receiver data should still be improved, particularly in environments with obstructions. The multipath effects are a major error source for the short baselines. The ground plate or the choke ring ground plane can reduce the multipath signals from the horizontal reflector (e. g. ground). However, the shieldings cannot reduce the multipath signals from the vertical reflectors (e. g. walls).Because multipath effects are spatially and temporally correlated, an algorithm is developed for reducing the multipath effect by considering the spatial correlations of the adjoined stations (see Zhang and Schwieger [24]). In this paper, an algorithm based on the temporal correlations will be introduced. The developed algorithm is based on the periodic behavior of the estimated coordinates and not on carrier phase raw data, which is easy to use. Because, for the users, coordinates are more accessible than the raw data. The multipath effect can cause periodic oscillations but the periods change over time. Besides this, the multipath effect’s influence on the coordinates is a mixture of different multipath signals from different satellites and different reflectors. These two properties will be used to reduce the multipath effect. The algorithm runs in two steps and iteratively. Test measurements were carried out in a multipath intensive environment; the accuracies of the measurements are improved by about 50 % and the results can be delivered in near-real-time (in ca. 30 minutes), therefore the algorithm is suitable for structural health monitoring applications.

scholarly journals Robustness against Chirp Signal Interference of On-Board Vehicle Geodetic and Low-Cost GNSS Receivers

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5257
Author(s):  
Franc Dimc ◽  
Polona Pavlovčič-Prešeren ◽  
Matej Bažec
Keyword(s):  
Interference Mitigation ◽  
Low Cost ◽  
Density Ratio ◽  
Autonomous Driving ◽  
Signal Interference ◽  
Noise Power ◽  
Reference Base ◽  
Noise Power Spectral Density ◽  
Processing Abilities ◽  
Gnss Receivers

Robust autonomous driving, as long as it relies on satellite-based positioning, requires carrier-phase-based algorithms, among other types of data sources, to obtain precise and true positions, which is also primarily true for the use of GNSS geodetic receivers, but also increasingly true for mass-market devices. The experiment was conducted under line-of-sight conditions on a straight road during a period of no traffic. The receivers were positioned on the roof of a car travelling at low speed in the presence of a static jammer, while kinematic relative positioning was performed with the static reference base receiver. Interference mitigation techniques in the GNSS receivers used, which were unknown to the authors, were compared using (a) the observed carrier-to-noise power spectral density ratio as an indication of the receivers’ ability to improve signal quality, and (b) the post-processed position solutions based on RINEX-formatted data. The observed carrier-to-noise density generally exerts the expected dependencies and leaves space for comparisons of applied processing abilities in the receivers, while conclusions on the output data results comparison are limited due to the non-synchronized clocks of the receivers. According to our current and previous results, none of the GNSS receivers used in the experiments employs an effective type of complete mitigation technique adapted to the chirp jammer.

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals Molecularly imprinted electrospun fiber membrane for colorimetric detection of hexanoic acid

e-Polymers ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 500-510
Author(s):  
Xiaoguang Ying ◽  
Jieyuan He ◽  
Xiao Li
Keyword(s):  
Low Cost ◽  
Electrospun Fiber ◽  
Colorimetric Detection ◽  
Hexanoic Acid ◽  
Bromocresol Purple ◽  
Fiber Membrane ◽  
Color Changes ◽  
Before And After ◽  
The Difference ◽  
Imagej Software

Abstract An imprinted electrospun fiber membrane was developed for the detection of volatile organic acids, which are key components of human body odor. In this study, hexanoic acid (HA) was selected as the target, polymethyl methacrylate (PMMA) was used as the substrate, and colorimetric detection of HA was achieved by a bromocresol purple (BCP) chromogenic agent. The results showed that the morphology of the fiber membrane was uniform and continuous, and it showed excellent selectivity and specificity to HA. Photographs of the color changes before and after fiber membrane adsorption were recorded by a camera and quantified by ImageJ software by the difference in gray value (ΔGray). This method is simple, intuitive, and low cost and has great potential for application in human odor analysis.

scholarly journals Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3917
Author(s):  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim ◽  
Ji-Soo Hwang
Keyword(s):  
Quantitative Analysis ◽  
Fluorescence Detection ◽  
High Performance ◽  
Low Cost ◽  
Dna Amplification ◽  
Fluorescent Detection ◽  
Rt Pcr ◽  
Commercial System ◽  
Intensity Changes ◽  
The Difference

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.

Compact Triple/Quadruple-Band Reconfigurable Monopole Antenna for Wireless Applications

2021 ◽  
pp. 2150277
Author(s):  
Asmaa Zugari ◽  
Wael Abd Ellatif Ali ◽  
Mohammad Ahmad Salamin ◽  
El Mokhtar Hamham
Keyword(s):  
Simple Structure ◽  
Low Cost ◽  
Ground Plane ◽  
Monopole Antenna ◽  
Pin Diode ◽  
Wireless Applications ◽  
Rectangular Patch ◽  
Antenna Performance ◽  
Compact Size ◽  
Multi Band

In this paper, a compact reconfigurable tri-band/quad-band monopole antenna is presented. To achieve the multi-band behavior, two right-angled triangles were etched in a conventional rectangular patch, and a partial ground plane is used. Moreover, the proposed multi-band antenna is printed on a low cost FR4 epoxy with compact dimensions of 0.23[Formula: see text], where [Formula: see text] is calculated at the lowest resonance frequency. To provide frequency agility, a metal strip which acts as PIN diode was embedded in the frame of the modified patch. The tri-band/quad-band antenna performance in terms of reflection coefficient, radiation patterns, peak gain and efficiency was studied. The measured results are consistent with the simulated results for both cases. The simple structure and the compact size of the proposed antenna could make it a good candidate for multi-band wireless applications.

scholarly journals Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Piezoelectric Ceramic ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
High Heart Rate ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

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