scholarly journals Vehicle-Type Detection Based on Compressed Sensing and Deep Learning in Vehicular Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4500 ◽  
Author(s):  
Yinghua Li ◽  
Bin Song ◽  
Xu Kang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Compressed Sensing ◽  
Vehicular Networks ◽  
Traffic Monitoring ◽  
Saliency Map ◽  
Monitoring And Control ◽  
Practical Applications ◽  
Vehicle Type ◽  
Traffic Regulation ◽  
Reference Information ◽  
Measurement Domain

Throughout the past decade, vehicular networks have attracted a great deal of interest in various fields. The increasing number of vehicles has led to challenges in traffic regulation. Vehicle-type detection is an important research topic that has found various applications in numerous fields. Its main purpose is to extract the different features of vehicles from videos or pictures captured by traffic surveillance so as to identify the types of vehicles, and then provide reference information for traffic monitoring and control. In this paper, we propose a step-forward vehicle-detection and -classification method using a saliency map and the convolutional neural-network (CNN) technique. Specifically, compressed-sensing (CS) theory is applied to generate the saliency map to label the vehicles in an image, and the CNN scheme is then used to classify them. We applied the concept of the saliency map to search the image for target vehicles: this step is based on the use of the saliency map to minimize redundant areas. CS was used to measure the image of interest and obtain its saliency in the measurement domain. Because the data in the measurement domain are much smaller than those in the pixel domain, saliency maps can be generated at a low computation cost and faster speed. Then, based on the saliency map, we identified the target vehicles and classified them into different types using the CNN. The experimental results show that our method is able to speed up the window-calibrating stages of CNN-based image classification. Moreover, our proposed method has better overall performance in vehicle-type detection compared with other methods. It has very broad prospects for practical applications in vehicular networks.

A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments

2017 ◽  
Vol 7 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Enrique Domínguez
Keyword(s):  
Video Surveillance ◽  
Extraction Process ◽  
Traffic Monitoring ◽  
Point Of View ◽  
Growing Neural Gas ◽  
Feature Extraction Method ◽  
Vehicle Type ◽  
Neural Gas ◽  
Detection And Tracking ◽  
Tracking Object

Automated video surveillance presents a great amount of applications and one of them is traffic monitoring. Vehicle type detection can provide information about the characteristics of the traffic flow to human traffic controllers in order to facilitate their decision-making process. A video surveillance system is proposed in this work to execute such classification. First of all, a foreground detection and tracking object process has been carried out. Once the vehicles are detected, a feature extraction method obtains the most significant features of this detected vehicles. When the extraction process is done, the vehicle types are determined by employing a set of Growing Neural Gas neural networks. The performance of the proposal has been analyzed from a qualitative and quantitative point of view by using a set of benchmark traffic video sequences, with acceptable results.

scholarly journals Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3702 ◽  
Author(s):  
Chiman Kwan ◽  
Bryan Chou ◽  
Jonathan Yang ◽  
Akshay Rangamani ◽  
Trac Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Compressive Sensing ◽  
Target Tracking ◽  
Low Power Consumption ◽  
Coded Aperture ◽  
Learning Approach ◽  
Residual Network ◽  
Practical Applications ◽  
Measurement Domain

Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.

scholarly journals Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986488 ◽  
Author(s):  
Junxin Chen ◽  
Jiazhu Xing ◽  
Leo Yu Zhang ◽  
Lin Qi
Keyword(s):  
Compressed Sensing ◽  
Sensor Network ◽  
Low Cost ◽  
Reconstruction Algorithm ◽  
Signal Acquisition ◽  
Reconstruction Algorithms ◽  
Practical Applications ◽  
Body Sensor Network ◽  
The Past ◽  
Electrocardiogram Signals

In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.

scholarly journals A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3531 ◽  
Author(s):  
Lorenzo Manoni ◽  
Claudio Turchetti ◽  
Laura Falaschetti ◽  
Paolo Crippa
Keyword(s):  
Compressed Sensing ◽  
Comparative Study ◽  
Low Power ◽  
Computational Methods ◽  
Low Cost ◽  
Reconstruction Algorithms ◽  
Practical Applications ◽  
Emg Signal ◽  
Wide Range ◽  
Independent Case

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

Modeling and Simulation of Urban Arterial Traffic Signal Coordinated Control

2014 ◽  
Vol 543-547 ◽  
pp. 1417-1422
Author(s):  
Wei Li ◽  
Xin Bi ◽  
Yun Xia Cao ◽  
Jin Song Du
Keyword(s):  
Traffic Congestion ◽  
Control Method ◽  
Coordinated Control ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Monitoring And Control ◽  
Total Delay ◽  
Arterial Traffic ◽  
Urban Arterial

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In order to reduce traffic delay, a novel urban arterial traffic signal coordinated control method is presented. The total delay of downstream and upstream vehicles is considered and the function describing the relationship between vehicles delay and signal offset among intersections is established. Finally, comparing the performance of traffic signal under method proposed in this paper with the traditional isolated traffic signal control method, the microscopic simulation results show that the method proposed in this paper has better performance in the aspect of reducing the vehicles delay. The offset model is tested in a simulation environment consisting of a core area of three intersections. It can be concluded that the proposed method is much more effective in relieving oversaturation in a network than the isolated intersection control strategy.

scholarly journals A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming

2020 ◽  
Vol 20 (5) ◽  
pp. 687-702 ◽  
Author(s):  
GEORGE BARYANNIS ◽  
ILIAS TACHMAZIDIS ◽  
SOTIRIS BATSAKIS ◽  
GRIGORIS ANTONIOU ◽  
MARIO ALVIANO ◽  
...  
Keyword(s):  
Real World ◽  
Traffic Monitoring ◽  
Qualitative Reasoning ◽  
Binary Relations ◽  
Logic Programs ◽  
Practical Applications ◽  
Knowledge Based ◽  
Important Barrier ◽  
Real World Problems

AbstractQualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs.

Video Object Segmentation

2009 ◽  
pp. 809-816 ◽  
Author(s):  
Ee Ping Ong ◽  
Weisi Lin
Keyword(s):  
Object Segmentation ◽  
Research Effort ◽  
Complete Solution ◽  
Cost Savings ◽  
Traffic Monitoring ◽  
Video Object Segmentation ◽  
Video Object ◽  
Home Office ◽  
Practical Applications ◽  
Object Based

Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.

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