Video Based Intelligent Transportation Systems – State of the Art and Future Development

Bommes, Michael; Fazekas, Adrian; Volkenhoff, Tobias; Oeser, Markus

doi:10.1016/j.trpro.2016.05.372

Cited by 49 publications

(19 citation statements)

References 23 publications

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“…Therefore, over the past decade, applying video-based systems has been a topic of great interest to researchers that can lead to faster development of transportation planning and traffic engineering applications [7]. Traffic flow control approaches like vehicle count and classification, traffic density estimation and speed measurement are some of the most popular applications of video-based ITS [8]. Among them, vehicle speed measurement is a wellknown process to recognize violations in video-based systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the last few years, cameras have become an important part of speed measurement systems. They have the benefits of lower maintenance costs, a wider field of view and providing better documentation proofs [8]. Moreover, considering recent improvements in image/video processing algorithms, there has been a great interest to utilize computer vision techniques to process the images of vehicles captured by these cameras, known as vision-based approaches [10].…”

Section: Introductionmentioning

confidence: 99%

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Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Tourani¹,

Shahbahrami²,

Akoushideh³

et al. 2019

IJIGSP

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Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to featurebased methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle's speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides realtime computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle's image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Tourani¹,

Shahbahrami²,

Akoushideh³

et al. 2019

IJIGSP

View full text Add to dashboard Cite

show abstract

“…Nevertheless, there are two main challenges of visual object tracking in IoT applications: 1) the low-cost camera may only produce lowframe-rate videos with low resolution [3] and 2) the limited storage and processing power of an embedded system can only process low-frame-rate videos. Nevertheless, there are two main challenges of visual object tracking in IoT applications: 1) the low-cost camera may only produce lowframe-rate videos with low resolution [3] and 2) the limited storage and processing power of an embedded system can only process low-frame-rate videos.…”

Section: Introductionmentioning

confidence: 99%

“…Tracking using a low-cost camera and an embedded system has received considerable research attention due to its wide applications in Internet of Things (IoT). Nevertheless, there are two main challenges of visual object tracking in IoT applications: 1) the low-cost camera may only produce lowframe-rate videos with low resolution [3] and 2) the limited storage and processing power of an embedded system can only process low-frame-rate videos. Based on the literature [4], low-cost cameras can produce 1 GB to 10 GB of data in a single day.…”

Section: Introductionmentioning

confidence: 99%

Dynamic swarm particle for fast motion vehicle tracking

2019

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Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low‐cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

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“…In this regard, Intelligent Transportation Systems (ITS) are defined as the means for collection of tools, facilities, and specializations, such as traffic management and telecommunications technologies in the form of coordinated instruments. ITS have various branches to provide the desired solutions to tackle the mentioned issues [3]. One of the main applications of ITS is the automatic speed measurement.…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Afshany¹,

Tourani²,

Shahbahrami³

et al. 2019

IJISA

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Nowadays, Intelligent Transportation Systems (ITS) are known as powerful solutions for handling traffic-related issues. ITS are used in various applications such as traffic signal control, vehicle counting, and automatic license plate detection. In the special case, video cameras are applied in ITS which can provide useful information after processing their outputs, known as Video-based Intelligent Transportation Systems (V-ITS). Among various applications of V-ITS, automatic vehicle speed measurement is a fast-growing field due to its numerous benefits. In this regard, visual appearancebased methods are common types of video-based speed measurement approaches which suffer from a computationally intensive performance. These methods repeatedly search for special visual features of vehicles, like the license plate, in consecutive frames. In this paper, a parallelized version of an appearance-based speed measurement method is presented which is real-time and requires lower computational costs. To acquire this, datalevel parallelism was applied on three computationally intensive modules of the method with low dependencies using NVidia's CUDA platform. The parallelization process was performed by the distribution of the method's constituent modules on multiple processing elements, which resulted in better throughputs and massively parallelism. Experimental results have shown that the CUDA-enabled implementation runs about 1.81 times faster than the main sequential approach to calculate each vehicle's speed. In addition, the parallelized kernels of the mentioned modules provide 21.28, 408.71 and 188.87 speed-up in singularly execution. The reason for performing these experiments was to clarify the vital role of computational cost in developing video-based speed measurement systems for real-time applications.

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Video Based Intelligent Transportation Systems – State of the Art and Future Development

Cited by 49 publications

References 23 publications

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Dynamic swarm particle for fast motion vehicle tracking

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

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