Speed Estimation and Abnormality Detection from Surveillance Cameras

Giannakeris, Panagiotis; Kaltsa, Vagia; Avgerinakis, Konstantinos; Briassouli, Alexia; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1109/cvprw.2018.00020

Cited by 26 publications

(42 citation statements)

References 24 publications

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“…Other approaches are feature‐based, for example, detecting the license plate [31, 41, 49, 60, 66, 68, 70, 76, 77, 80, 82, 94, 122, 125] or other features of the vehicle [38, 39, 45, 49, 57–59, 69, 71, 79, 87, 91, 95, 96, 98, 99, 110–112, 116, 117, 123]. The use of learning‐based methods to recognize vehicles in images has been growing recently [19, 85, 88–91, 98, 100, 101, 104–106, 115, 120, 126, 127, 129].…”

Section: Taxonomymentioning

confidence: 99%

“…Surprisingly, and contrary to the evidence described in subsection 6.3, most of the works propose to use consecutive measurements (at frame

t

and at frame

t + 1

) to calculate the speed values for each vehicle [14, 18, 19, 22, 25, 33, 35–37, 41, 46, 48, 50, 55, 65, 67, 68, 71–76, 78, 82, 83, 85, 88, 89, 92, 96, 100, 103, 120, 129]. As illustrated in Figure 12, this technique tends to provide noisy values since the distance errors are of the same order of magnitude as the distance traveled by the vehicle.…”

Section: Vehicle Distance and Speed Estimationmentioning

confidence: 99%

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Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.

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Section: Taxonomymentioning

confidence: 99%

“…Surprisingly, and contrary to the evidence described in subsection 6.3, most of the works propose to use consecutive measurements (at frame

t

and at frame

t + 1

Section: Vehicle Distance and Speed Estimationmentioning

confidence: 99%

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…A novel vehicle classification technique has been developed based on multiple pavement strains caused by moving traffic loads [33]. The overlap of vehicle classification feature parameters belonging to different classes suggested the need to use a pattern recognition technique for separating vehicles into different groups [34]. To improve classification accuracy and robustness centralized and distributed fusion schemes based on two popular SVMs multi-class algorithms were used as fusion multiple sensor data [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Dewi¹

2021

IJEM

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The background of this research raises the problem of the development of smart transportation in terms of monitoring and enforcement of traffic on the highway, with the proposed system that will help many parties such as the police and the government. The system that is running is to develop a system using CCTV that is placed on every corner of the capital city to replace the police in carrying out road surveillance and law enforcement against lawbreakers, especially in the traffic sector. The method used in this research is by using literature review of many previous research journals, by reading many journals will be able to add knowledge and can deepen ongoing research. The problem raised in this research is finding solutions to problems in the transportation sector, especially smart transportation, using smart transportation will be able to connect all systems that have been made. This research produces a system proposal that can be used in further research and can be applied in terms of the development of smart cities, especially smart transportation.

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“…IoT in a mode of transportation is very important because there are existing media in several modes of transportation, such as online motorcycle taxis, with IoT media, so data transmission and data reception can be sent quickly and can be processed immediately [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Greedy Algorithm on Traffic Violation Enforcement

Dewi¹,

Putra²

2021

IJEME

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The background of this research is how the application of the algorithm in traffic control systems, with the Greedy algorithm, the system will decide what action and what punishment will be given to traffic offenders on the highway, with the Greedy algorithm, the decisions taken will be based on data and facts. based on existing laws, so decisions made based on law and the human side cannot influence the decisions to be taken by the system based on the application of the Greedy algorithm. The research method used in this study uses literature reviews by reading many previous research journals, it will be able to add knowledge and deepen the research we are doing this time, with the literature review method, we will be able to find new problems and can be used as new research because The literature review is very helpful for our research this time. The system that is being used is using CCTV and can determine what decisions and punishments will be given to traffic offenders, through evidence based on images taken by cameras placed at red lights or corners of the capital's highway, the system this has been active effectively but with the implementation of the algorithm will increase. This research will produce a system proposal and be able to find out whether the application of the Greedy algorithm is correct and can help the current system by implementing the algorithm, so the existing system is more perfect. The main contribution of this research is that the use of the Greedy algorithm can help control the traffic system to enforce the law.

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Speed Estimation and Abnormality Detection from Surveillance Cameras

Cited by 26 publications

References 24 publications

Vision‐based vehicle speed estimation: A survey

Vision‐based vehicle speed estimation: A survey

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Application of Greedy Algorithm on Traffic Violation Enforcement

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