A Low-Complexity Vision-Based System for Real-Time Traffic Monitoring

Engel, Isaac; Martin, Juan Carlos De; Barco, Raquel

doi:10.1109/tits.2016.2603069

Cited by 48 publications

(17 citation statements)

References 14 publications

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“…This system is designed on ARM/FPGA processor. Negative prospect is the high cost of systems [12] like these when installed in urban areas. Mostly widely used sensors in traffic monitoring are surveillance video cameras that provide videos for the purpose of detection and counting vehicles, but there exists a gap including occlusion, cloudy weather, shadows, and limited view.…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

et al. 2020

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In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.

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Section: Related Workmentioning

confidence: 99%

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

et al. 2020

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“…Techniques employed for road traffic density analysis mainly rely on motion detection or background modeling and subtraction to detect vehicles [10], thus limited by their application only to free-flowing traffic scenes or scenes with static backgrounds. Also segmentation results when used with traditional static background subtraction method were not reliable since changing illumination conditions were not factored [46]. Therefore, dynamic background modeling offered advantage of handling changing scene conditions [45,46], though this method could not be used for stationary traffic monitoring.…”

Section: Table III Advantages and Disadvantages Of Algorithms Used Imentioning

confidence: 99%

“…Also segmentation results when used with traditional static background subtraction method were not reliable since changing illumination conditions were not factored [46]. Therefore, dynamic background modeling offered advantage of handling changing scene conditions [45,46], though this method could not be used for stationary traffic monitoring. In fact, many proposed approaches for traffic monitoring have not been tested for performance under dissimilar illumination conditions.…”

Section: Table III Advantages and Disadvantages Of Algorithms Used Imentioning

confidence: 99%

Image Processing in Intelligent Traffic Management

2019

ijrte

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Traffic monitoring and traffic control have always been challenging tasks. Intelligent Transportation Systems (ITS) based on wide range of technologies have certain practical challenges in their application and implementation. Video surveillance has proven advantageous over traditional systems based on inductive loops sensors and detectors for traffic monitoring. Accurate traffic density estimation which is basic to tackling traffic congestions requires detection of vehicles, assessing their speed, and tracking vehicles passing through surveillance zones. Image processing techniques require processing of large number of image frames for real-time applications in traffic management. More efficient and less costly image processing techniques for accurate vehicle detection and density determination are required for developing more effective traffic management systems. There is a need for developing algorithms with robust performance under heavy traffic loads and varied environmental conditions. Developments in artificial intelligence offer new vistas in image processing for regulation and management of traffic by signal control mechanisms and creation of neural networks for unhindered traffic flow.

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“…Extraction and categorization of vast amounts of data require expensive and sophisticated software. Processing the live feed for even a single camera requires a dedicated CPU [27]. More performance requires computer accelerators.…”

Section: Related Workmentioning

confidence: 99%

Performance and energy-efficient implementation of a smart city application on FPGAs

Arif

Barrigon

Gregoretti

et al. 2018

J Real-Time Image Proc

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The continuous growth of modern cities and the request for better quality of life, coupled with the increased availability of computing resources, lead to an increased attention to smart city services. Smart cities promise to deliver a better life to their inhabitants while simultaneously reducing resource requirements and pollution. They are thus perceived as a key enabler to sustainable growth. Out of many other issues, one of the major concerns for most cities in the world is traffic, which leads to a huge waste of time and energy, and to increased pollution. To optimize traffic in cities, one of the first steps is to get accurate information in real time about the traffic flows in the city. This can be achieved through the application of automated video analytics to the video streams provided by a set of cameras distributed throughout the city. Image sequence processing can be performed both peripherally and centrally. In this paper, we argue that, since centralized processing has several advantages in terms of availability, maintainability and cost, it is a very promising strategy to enable effective traffic management even in large cities. However, the computational costs are enormous, and thus require an energy-efficient High-Performance Computing approach. Field Programmable Gate Arrays (FPGAs) provide comparable computational resources to CPUs and GPUs, yet require much lower amounts of energy per operation (around 6 × and 10× for the application considered in this case study). They are thus preferred resources to reduce both energy supply and cooling costs in the huge datacenters that will be needed by Smart Cities. In this paper, we describe efficient implementations of high-performance algorithms that can process traffic camera image sequences to provide traffic flow information in real-time at a low energy and power cost. Keywords Smart city • Image processing • Background subtraction • Lucas-Kanade • High-level synthesis • Field programmable gate array • Graphical processing unit

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A Low-Complexity Vision-Based System for Real-Time Traffic Monitoring

Cited by 48 publications

References 14 publications

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

Image Processing in Intelligent Traffic Management

Performance and energy-efficient implementation of a smart city application on FPGAs

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