Video Stream Mining for On-Road Traffic Density Analytics

2012 ◽  
pp. 182-194
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

Video Stream Mining for On-Road Traffic Density Analytics

2013 ◽  
pp. 1019-1030
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

Head motion coefficient-based algorithm for distracted driving detection

2019 ◽  
Vol 53 (2) ◽  
pp. 171-188 ◽  
Author(s):  
Kwok Tai Chui ◽  
Wadee Alhalabi ◽  
Ryan Wen Liu
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Video Stream ◽  
Detection Algorithm ◽  
Distracted Driving ◽  
Head Motion ◽  
Content Type ◽  
Cell Phone Use ◽  
The Road ◽  
Video Frames

PurposeConcentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.Design/methodology/approachThe system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.FindingsThe accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.Originality/valueThe system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

scholarly journals INTELLIGENT CAR PARKING SYSTEM

2014 ◽  
pp. 261-267
Author(s):  
S. AVINASH ◽  
SNEHA MITTRA ◽  
SUDIPTA NAYAN GOGOI ◽  
C. SURESH
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Empty Space ◽  
The West ◽  
The Road ◽  
Design And Implementation ◽  
Dc Motors ◽  
Parking System ◽  
Machine Interface ◽  
On The Road

Due to the proliferation in the number of vehicles on the road, traffic problems are bound to exist. This is due to the fact that the current transportation infrastructure and car parking facility developed are unable to cope with the influx of vehicles on the road. In India, the situation are made worse by the fact that the roads are significantly narrower compared to the west. Therefore problems such as traffic congestion and insufficient parking space inevitably crops up. In his paper we describe an Intelligent Car Parking System, which identifies the available spaces for parking using sensors, parks the cars in an identified empty space and gets the car back from its parked space without the help of any human personnel. A Human Machine Interface (HMI) helps in entering a unique identification number while entry of any car which helps in searching for the space where the car is parked while exit. An Indraconrol L10 PLC controls the actions of the parking system. The PLC is used to sequence the placing and fetching of the car via DC motors. We have implemented a prototype of the system. The system evaluation demonstrates the effectiveness of our design and implementation of car parking system.

scholarly journals Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Yan Zheng ◽  
Yanran Li ◽  
Chung-Ming Own ◽  
Zhaopeng Meng ◽  
Mengya Gao
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Traffic Congestion ◽  
Field Experiments ◽  
Road Traffic ◽  
Markov Chain Model ◽  
Chain Model ◽  
The Road ◽  
Shortest Path Algorithms ◽  
On The Road

With the explosive growth of vehicles on the road, traffic congestion has become an inevitable problem when applying guidance algorithms to transportation networks in a busy and crowded city. In our study, the authors proposed an advanced prediction and navigation models on a dynamic traffic network. In contrast to the traditional shortest path algorithms, focused on the static network, the first part of our guiding method considered the potential traffic jams and was developed to provide the optimal driving advice for the distinct periods of a day. Accordingly, by dividing the real-time Global Positioning System data of taxis in Shenzhen city into 50 regions, the equilibrium Markov chain model was designed for dispatching vehicles and applied to ease the city congestion. With the reveals of our field experiments, the traffic congestion of city traffic networks can be alleviated effectively and efficiently, the system performance also can be retained.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals A Framework to Reduce Road Congestion by means of Data Analytics

2021 ◽  
Vol 4 (1) ◽  
pp. 287-297
Author(s):  
Anosha Arooj Yousaf ◽  
Najia Saher ◽  
Faisal Shahzad ◽  
Sara Fareed
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Data Analytics ◽  
Road Traffic ◽  
Image Data ◽  
The Road ◽  
Road Planning ◽  
On The Road ◽  
Critical Traffic ◽  
Processing Techniques

The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.

scholarly journals Applications of Surveying and Geoinformatics for Planning New Routes to Solve Traffic Congestion in part of Minna Metropolis (Kpakungu, a case study)

2019 ◽  
Vol 3 (1) ◽  
pp. 149-160
Author(s):  
I. C. Onuigbo ◽  
T. Adewuyi ◽  
J. O. Odumosu ◽  
G. A. Oluibukun
Keyword(s):  
Land Use ◽  
Traffic Congestion ◽  
Road Network ◽  
Road Traffic ◽  
Urban Land Use ◽  
Land Use Pattern ◽  
The Road ◽  
National Examination ◽  
Traffic Volumes ◽  
On The Road

The volume of traffic generated by land-use pattern varies during different periods of the day but there is usually a predictable pattern of such traffic volumes. Most often, the structure of urban land-use fails to provide easy and convenient traffic movement, which in the case of the study area is usually that of vehicles and pedestrian traffic. The fact is that Minna is presently experiencing rapid urban growth. Both the authorities and citizens seem to simply ignore this and its impact on human existence. The research is based on Road Traffic Network Analysis in Minna, to develop a road network map and determine the causes of Traffic Congestion in Kpakungu specifically. Quickbird satellite imagery was used in analyzing and mapping out the existing road network within the study area. Field survey aspects involving measuring of roads, traffic count, coordinates captured were also undertaken. It was discovered that the causes of the traffic pressure in the study area was as a result of the relocation of Federal University of Technology, Minna to its permanent site in Gidan Kwanu and the relocation of National Examination Council(NECO) Headquarter. Majority of the traffic pressure in the area were as a result of vehicles coming from Maikunkele, Bosso, Maitumbi, Minna central, Dutsen Kura, Chanchaga, Tunga, Sahuka-kahuta and BarikinSale going to Bida, Gidan-Kwanu or NECO office. It was concluded that alternative roads should be provided for vehicle diversion to limit the congestion of traffic on the road.

scholarly journals A Route Reservation Approach for an Autonomous Vehicles Routing Problem

2018 ◽  
Vol 220 ◽  
pp. 02004 ◽  
Author(s):  
Anton Agafonov ◽  
Aleksandr Borodinov
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Routing Problem ◽  
Microscopic Traffic Simulation ◽  
The Road ◽  
On The Road ◽  
Road Segments

Autonomous vehicle development is one of many trends that will affect future transport demands and planning needs. Autonomous vehicles management in the context of an intelligent transportation system could significantly reduce the traffic congestion level and decrease the overall travel time in a network. In this work, we investigate a route reservation architecture to manage road traffic within an urban area. The routing architecture decomposes road segments into time and spatial slots and for every vehicle, it makes the reservation of the appropriate slots on the road segments in the selected route. This approach allows to predict the traffic in the network and to find the shortest path more precisely. We propose to use a rerouting procedure to improve the quality of the routing approach. Experimental study of the routing architecture is conducted using microscopic traffic simulation in SUMO package.

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