scholarly journals A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3243 ◽  
Author(s):  
Marcin Bernas ◽  
Bartłomiej Płaczek ◽  
Wojciech Korski ◽  
Piotr Loska ◽  
Jarosław Smyła ◽  
...  
Keyword(s):  
Road Traffic ◽  
Low Cost ◽  
Pedestrian Detection ◽  
Traffic Monitoring ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Active Sensors ◽  
Wide Range ◽  
Passive Sensors ◽  
Supporting Structures

This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

scholarly journals Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1987 ◽  
Author(s):  
Rocío Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Carlos Novillo
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Principal Component ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Types ◽  
Statistical Parameters ◽  
Wide Range ◽  
The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

scholarly journals DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2743
Author(s):  
Kun Sun ◽  
Yi Liang ◽  
Xiaorui Ma ◽  
Yuanyuan Huai ◽  
Mengdao Xing
Keyword(s):  
Target Detection ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Cost ◽  
Detection Performance ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Computation Complexity ◽  
Learning Techniques

Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).

scholarly journals Estimation of Traffic Occupancy using Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 7291-7295
Author(s):  
M. U. Farooq ◽  
A. Ahmed ◽  
S. M. Khan ◽  
M. B. Nawaz
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Percentage Difference ◽  
Novel Approach ◽  
Removal Technique

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Moving Vehicle Detection of Airborne Video on SURF

2013 ◽  
Vol 734-737 ◽  
pp. 2815-2818
Author(s):  
Hui Liu ◽  
Chun Xian Gao ◽  
Xing Hao Ding ◽  
Zhe Zeng
Keyword(s):  
Detection Efficiency ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Normal Vector ◽  
Detection Accuracy ◽  
External Interference ◽  
Moving Vehicle ◽  
Low Level ◽  
Wide Range ◽  
Moving Vehicle Detection

Due to the high mobility, a wide range of monitoring, air mobile platform-based vehicle detection and tracking system is becoming core of the investigation and the monitoring. Self-motion of the camera and external interference caused by the low-level platform led to instability of the obtained video and affect the correct detection of moving targets and subsequent analysis. For the characteristics for low-level video, an image stabilization algorithm based on SURF combined with normal vector of optical flow is proposed to solve moving vehicle detection low-altitude video. From the experimental results can be seen: (1) compared to other moving vehicle detection methods, the method proposed can get better detection efficiency and detection accuracy; (2) in the complex context, this method can effectively detect moving vehicles. The experiments show that this method has some theoretical and application value of space-based video moving target detection.

Canny Edge Detection Method and its Application

2011 ◽  
Vol 50-51 ◽  
pp. 483-487 ◽  
Author(s):  
Yan Bing Liang ◽  
Xiao Li Meng ◽  
Shu Jiang An
Keyword(s):  
Edge Detection ◽  
Relative Position ◽  
Traffic Monitoring ◽  
Detection Methods ◽  
General Distribution ◽  
Digital Cameras ◽  
Test Models ◽  
Wide Range ◽  
Edge Detection Method ◽  
Image Planes

Digital Cameras positioning has a wide range of application in the aspect of traffic monitoring (e-police).In this paper, the author builds and solves the mathematical model of positioning of monocular by edge detection methods and physical principles of optical imaging of Gauss, and offers a distortion error algorithm to test models, and finally sets up to solve the problem of relative position of multi-cameras. The introduction of distortion error algorithm, could be used to quantitatively examine the models in the first two steps. In accordance with the image situation of multi-image planes, the relative position between the cameras could be determined. This model of camera generates Multi-Vision Inspecting Technique of general distribution of the relative position. Relative position can be figured out if only the parameters of the pictures to be determined are available to determine the inner and outer parameters of the camera.

scholarly journals LPWAN-Based Hybrid Backhaul Communication for Intelligent Transportation Systems: Architecture and Performance Evaluation

2020 ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Performance Evaluation ◽  
Low Power ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Wide Range

Abstract Increased number of the vehicles on the streets around the world has led to several problems including traffic congestion in many regions. Intelligent Transportation Systems (ITSs) are a viable solution for this problem by implementing efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide wide-range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In order to to understand the feasibility of using LPWAN for ITS, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end to end delay per user. Simulation of Urban MObility (SUMO) has been used for realistic traffic generation and a Python-based program has been developed. This program has the ability to exchange live data with SUMO for communication performance evaluation. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

scholarly journals Progressive Multi-Scale Vision Transformer for Facial Action Unit Detection

2022 ◽  
Vol 15 ◽  
Author(s):  
Chongwen Wang ◽  
Zicheng Wang
Keyword(s):  
Affective Computing ◽  
Receptive Fields ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Action Unit ◽  
Facial Action ◽  
Facial Landmarks ◽  
Feature Representations ◽  
Multi Scale ◽  
Wide Range

Facial action unit (AU) detection is an important task in affective computing and has attracted extensive attention in the field of computer vision and artificial intelligence. Previous studies for AU detection usually encode complex regional feature representations with manually defined facial landmarks and learn to model the relationships among AUs via graph neural network. Albeit some progress has been achieved, it is still tedious for existing methods to capture the exclusive and concurrent relationships among different combinations of the facial AUs. To circumvent this issue, we proposed a new progressive multi-scale vision transformer (PMVT) to capture the complex relationships among different AUs for the wide range of expressions in a data-driven fashion. PMVT is based on the multi-scale self-attention mechanism that can flexibly attend to a sequence of image patches to encode the critical cues for AUs. Compared with previous AU detection methods, the benefits of PMVT are 2-fold: (i) PMVT does not rely on manually defined facial landmarks to extract the regional representations, and (ii) PMVT is capable of encoding facial regions with adaptive receptive fields, thus facilitating representation of different AU flexibly. Experimental results show that PMVT improves the AU detection accuracy on the popular BP4D and DISFA datasets. Compared with other state-of-the-art AU detection methods, PMVT obtains consistent improvements. Visualization results show PMVT automatically perceives the discriminative facial regions for robust AU detection.

scholarly journals A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7279
Author(s):  
Yao Wang ◽  
Peizhi Yu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
High Speed ◽  
Large Scale ◽  
Feature Fusion ◽  
Low Cost ◽  
Obstacle Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Real Time Processing

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing.

scholarly journals A Novel Algorithm for Detecting Pedestrians on Rainy Image

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 112
Author(s):  
Yuhang Liu ◽  
Jianxiao Ma ◽  
Yuchen Wang ◽  
Chenhong Zong
Keyword(s):  
State Of The Art ◽  
Pedestrian Detection ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Traffic Data ◽  
Average Precision ◽  
Synthetic Datasets ◽  
Light Medium

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.

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