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  • 1.
    Dahl, Mattias
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Javadi, Mohammad Saleh
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Analytical modelling for video-based vehicle speed measurement framework2019Inngår i: Optik (Stuttgart), ISSN 0030-4026, E-ISSN 1618-1336Artikkel i tidsskrift (Fagfellevurdert)
  • 2.
    Javadi, Mohammad Saleh
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Computer Vision Algorithms for Intelligent Transportation Systems Applications2018Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    In recent years, Intelligent Transportation Systems (ITS) have emerged as an efficient way of enhancing traffic flow, safety and management. These goals are realized by combining various technologies and analyzing the acquired data from vehicles and roadways. Among all ITS technologies, computer vision solutions have the advantages of high flexibility, easy maintenance and high price-performance ratio that make them very popular for transportation surveillance systems. However, computer vision solutions are demanding and challenging due to computational complexity, reliability, efficiency and accuracy among other aspects.

    In this thesis, three transportation surveillance systems based on computer vision are presented. These systems are able to interpret the image data and extract the information about the presence, speed and class of vehicles, respectively. The image data in these proposed systems are acquired using Unmanned Aerial Vehicle (UAV) as a non-stationary source and roadside camera as a stationary one. The goal of these works is to enhance the general performance in accuracy and robustness of the systems with variant illumination and traffic conditions.

    This is a compilation thesis in systems engineering consists of three parts. The red thread through each part is a transportation surveillance system. The first part presents a change detection system using aerial images of a cargo port. The extracted information shows how the space is utilized at various times for further management and development of the port. The proposed solution can be used at different viewpoints and illumination levels e.g. sunset. The method is able to transform the images taken from different viewpoints and match them together and then using a proposed adaptive local threshold to detect discrepancies between them. In the second part, a vision-based vehicle's speed estimation system is presented. The measured speeds are essential information for law enforcement as well as estimation of traffic flow at certain points on the road. The system employs several intrusion lines to extract the movement pattern of each vehicle (non-equidistant sampling) as an input feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also taken into account to address the uncertainty in the measurements and to obtain the probability density function of the vehicle's speed. In the third part, a vehicle classification system is provided to categorize vehicles into “private cars", “light trailers", “lorry or bus" and “heavy trailer". This information can be used by authorities for surveillance and development of the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The system has been constructed using prior knowledge of traffic regulations regarding each class of vehicle in order to enhance the classification performance.

  • 3.
    Javadi, Mohammad Saleh
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Change detection in aerial images using a Kendall's TAU distance pattern correlation2016Inngår i: PROCEEDINGS OF THE 2016 6TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP), IEEE, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Change detection in aerial images is the core of many remote sensing applications to analyze the dynamics of a wide area on the ground. In this paper, a remote sensing method is proposed based on viewpoint transformation and a modified Kendall rank correlation measure to detect changes in oblique aerial images. First, the different viewpoints of the aerial images are compromised and then, a local pattern descriptor based on Kendall rank correlation coefficient is introduced. A new distance measure referred to as Kendall's Tau-d (Tau distance) coefficient is presented to determine the changed regions. The developed system is applied on oblique aerial images with very low aspect angles that obtained using an unmanned aerial vehicle in two different days with drastic change in illumination and weather conditions. The experimental results indicate the robustness of the proposed method to variant illumination, shadows and multiple viewpoints for change detection in aerial images.

  • 4.
    Javadi, Mohammad Saleh
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vehicle speed measurement model for video-based systems2019Inngår i: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 76, s. 238-248Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Advanced analysis of road traffic data is an essential component of today's intelligent transportation systems. This paper presents a video-based vehicle speed measurement system based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model introduced to generate the pdf (probability density function) of a vehicle's speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circumstances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios.

  • 5.
    Javadi, Mohammad Saleh
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Kulesza, Wlodek
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
    Design of a video-based vehicle speed measurement system: an uncertainty approach2019Inngår i: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 2018, pp. 44-49., IEEE, 2019, artikkel-id 8640964Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. This paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.

  • 6.
    Javadi, Mohammad Saleh
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rameez, Muhammad
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features2018Inngår i: Procedia Computer Science, Elsevier, 2018, Vol. 126, s. 7s. 1344-1350Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensorS. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifierS. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearanceS. In addition, it is fast and efficient for big data analysiS.

  • 7.
    Pettersson, Mats
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Javadi, Mohammad Saleh
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Future Satellite and Drone Monitoring of the Baltic‐Adriatic Corridor,Harbors, and Motorways of the Sea2019Rapport (Annet vitenskapelig)
1 - 7 of 7
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