Video-based tracking of vehicles using multiple time-spatial images

Mithun, Niluthpol Chowdhury; Howlader, Tamanna; Rahman, S. M. Mahbubur

doi:10.1016/j.eswa.2016.06.020

Cited by 27 publications

(7 citation statements)

References 60 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motion-based models also play a significant part in detecting vehicles. Optical flow, a typical pattern recognition tool, has been used to extract moving vehicle targets [14] and track on-road vehicles in video sequences [15].…”

Section: Model-based Methodsmentioning

confidence: 99%

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

Yang¹

2017

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

Robust and efficient vehicle detection from images is an important task in Intelligent Transportation Systems. With the development of computer vision techniques and consequent accessibility of video image data, new applications have been enabled to on-road vehicle detection algorithms. This paper provides a review of the literature in vehicle detection under varying environments. Due to the variability of on-road driving environments, vehicle detection may face different problems and challenges. Therefore, many approaches have been proposed to detect vehicles, such as feature-based method and model-based methods. In addition, special illumination, weather and driving scenarios are discussed in terms of methodology and quantitative evaluation. In the future, works should be focused on robust vehicle detection approaches for various on-road conditions.

show abstract

Section: Model-based Methodsmentioning

confidence: 99%

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

Yang¹

2017

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

show abstract

“…Vehicle tracking aims to re-identify vehicles, derive vehicle trajectories, and predict vehicle positions and motions based on the patterns of previous positions and motions [9]. The methods of video-based tracking can be systematically classified as a contour-based method establishing the outline of moving objects and updating outlines according to the motion of an object [19]; feature-based methods concentrating on tracking significant features of the vehicle and the distribution of these features [10]; and framework-based methods in which objects in a framework are assumed to move in accordance with certain distribution patterns like position and velocity [20].…”

Section: Vehicle Trackingmentioning

confidence: 99%

“…The Hungarian algorithm [21] is an optimization algorithm used to solve the assignment problem that deals with the allocation of resources to activities, seeking for minimum cost or maximum profit. The authors in Reference [20] used a Kalman filter to predict the centroid of the observed vehicle on successive frames, calculate measurement errors and prediction errors, and dynamically update predictions. Our system shows that an ensemble method that integrates these two approaches provides higher accuracy.…”

Section: Vehicle Trackingmentioning

confidence: 99%

Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage

Zheng

Chen

et al. 2019

Infrastructures

View full text Add to dashboard Cite

Real-time traffic monitoring is essential in many novel applications, from traffic management to smart navigation systems. The large number of traffic cameras being integrated into urban infrastructures has enabled efficient traffic monitoring as an intervention in reducing traffic accidents and related casualties. In this paper, we focus on the problem of the automatic detection of anomalous driving behaviors, e.g., speeding or stopping on a bike lane, by using the traffic-camera feed that is available online. This can play an important role in personalized route-planning applications where, for instance, a user wants find the safest paths to get to a destination. We present an integrated system that accurately detects, tracks, and classifies vehicles using online traffic-camera feed.

show abstract

“…Instead of driving activities control system, there is also a requirement for developing driving assistance systems and that is considered to be a challenging task [12,13]. To do this task, there are many algorithms proposed to track the vehicles in the motorway driving [14]. The advantage of these systems is their real-time performance.…”

Section: Related Workmentioning

confidence: 99%

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Abbas¹

2019

ijacsa

View full text Add to dashboard Cite

Vehicle monitoring is a challenging task for videobased intelligent transportation system (V-ITS). Nowadays, the V-ITS system has a significant socioeconomic impact on the development of smart cities and always demand to monitor different traffic parameters. It noticed that traffic accidents are exceeded throughout the world with the percentage of 1.7%. The increase in accidents and the percentage of deaths are due to the people that don't abide by the traffic rules. To address these challenges, an improved V-ITS system is developed in this paper to detect and track vehicles and driver's activities during highway driving. This improved V-ITS system is capable to do automatic traffic management that saves traffic accidents. It provides the feature of a real-time detection algorithm for driver immediate line overrun, speed limit overrun and yellow-line driving. To develop this V-ITS system, a pre-trained convolutional neural network (CNN) model with 4-layer architecture was developed and then deep-belief network (DBN) model was utilized to recognize illegal activities. To implement V-ITS system, OpenCV and python tools are mainly utilized. The GRAM-RTM online free data sets were used to test the performance of V-ITS system. The overall significance of this intelligent V-ITS system is comparable to other state-of-the-art systems. The real-time experimental results indicate that the V-ITS system can be used to reduce the number of accidents and ensure the safety of passengers as well as pedestrians.

show abstract

Video-based tracking of vehicles using multiple time-spatial images

Cited by 27 publications

References 60 publications

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage

V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Contact Info

Product

Resources

About