Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

Vishnu, V. C. Maha; Rajalakshmi, M.; Nedunchezhian, R.

doi:10.1007/s10586-017-0974-5

Cited by 51 publications

(21 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Histogram of motion orientation and magnitude are also proposed as spatio‐temporal features [47]. A histogram of flow gradient (HFG) descriptor is also proposed [48], which is derived from the histogram of gradient (HOG) descriptor by replacing static gradient with optical flow gradient. However, the underlying structure of HOG descriptor is not invariant to scaling and rotations.…”

Section: Related Workmentioning

confidence: 99%

Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine

2019

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine

2019

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Background subtraction was a major method used to accomplish this task [2,3,4]. Gaussian Mixture Model based background subtraction [5], Hybrid support vector machine (SVM with extended Kalman filter) [6] and particle filter-based trackers [7] were also used. These methods did not fetch good results during heavy traffic conditions due to overlapping off vehicles and predicted wrong bounding boxes.…”

Section: Fig 1 Artificial Intelligence and Subdomainsmentioning

confidence: 99%

Intelligent Traffic Management System

Singh*¹,

Unadkat²,

Kanani³

2019

IJRTE

View full text Add to dashboard Cite

Urbanization has presented opportunities of progress which has attracted people from rural areas to the cities thus leading to mass migration. This migration has been going on for decades all around the globe and has reached a point of saturation. The area of the city remains the same but the population density has increased multiple times. Commuting for work is a scene of chaos on the roads. Though there are modes of public transport, roadway is the major mode of commute and the load on roadways is ever increasing due to the rise in population. There is hardly any scope to expand the area of the roadways. The rise in the number of vehicles each year has saturated the capacity the roads were built to carry. This leads to congestion and long hours of traffic on a daily basis which tests the patience of citizens. This provokes the daily commuters to violate the traffic rules which may sometimes amount to grave accidents. Even on Highways, the empty roads entice drivers to experience the thrill of speed overlooking the fact that they are putting themselves at risk. There have been regulations imposed to reduce the chance of an accidents by implementing rules and levying heavy fines on traffic violations. Traffic cameras have been installed all around the city to monitor for traffic violations and get hold of violators. With the technological advancements to store and process large chunks of data efficiently using techniques like Deep Learning and Computer Vision, this paper proposes an automated system to detect Traffic Violations using YOLOv3 to detect and track vehicles and save a snapshot in case a violation is committed.

show abstract

“…Subsequently, vehicle tracking algorithms have been created using the Kalman filter to obtain improved estimates of traffic densities and to facilitate the data acquirement [4,7,10,[33][34][35]. There are also new algorithms for vehicle detection that combine the Kalman filter with other methods, such as the hybrid median filter [13], but require a more complex programming language.…”

Section: The First Test Case: Ideal Dam Break Over Movable Bedmentioning

confidence: 99%

“…Although automatic vehicle counting software already exist, and some of them use the Kalman filter [7,[10][11][12][13][14], their price is elevated, and in some cases, may be prohibitive. An alternative vehicle counting tool for low budget conditions is proposed, which involves video-image processing with the Kalman filter in Matlab.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

et al. 2017

View full text Add to dashboard Cite

This study proposes an alternative and economical tool to estimate traffic densities, via video-image processing adapting the Kalman filter included in the Matlab code. Traffic information involves acquiring data for long periods of time at stationary points. Vehicle counting is vital in modern transport studies, and can be achieved by using different techniques, such as manual counts, use of pneumatic tubes, magnetic sensors, etc. In this research however, automatic vehicle detection was achieved using image processing, because it is an economical and sometimes even faster option. Commercial automatic vehicle detection and tracking programs/applications already exist, but their use is typically prohibitive due to their high cost. Large cities can obtain traffic recordings from surveillance cameras and process the information, but it is difficult for smaller towns without such infrastructure or even assigned budget. The proposed tool was developed taking into consideration these difficult situations, and it only requires users to have access to a fixed video camera placed at an elevated point (e.g. a pedestrian bridge or a light pole) and a computer with a powerful processor; the images are processed automatically through the Kalman filter code within Matlab. The Kalman filter predicts random signals, separates signals from random noise or detects signals with the presence of noise, minimizing the estimated error. It needs nevertheless some adjustments to focus it for vehicle counting. The proposed algorithm can thus be adapted to fit the users' necessities and even the camera's position. The use of this algorithm allows to obtain traffic data and may help small cities´ decision makers dealing with present and future urban planning and the design or installment of transportation systems.

show abstract

Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

Cited by 51 publications

References 26 publications

Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine

Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine

Intelligent Traffic Management System

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

Contact Info

Product

Resources

About