| 1. |
- Amini, Ehsan, et al.
(author)
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Saliency Map Generation Based on Human Level Performance
- 2024
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In: IEEE Gaming, Entertainment, and Media Conference, GEM 2024. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350374537
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Conference paper (peer-reviewed)abstract
- Generating precise saliency maps from eye tracker fixation points is a challenging task influenced by environmen-tal factors and the choice of evaluation metrics. This paper presents a novel, sustainable, scale-invariant, and sampling-independent method for converting fixation points into saliency maps. Leveraging the inherent predictability of human behavior, the proposed method ensures the highest compatibility with the chosen evaluation metric. Moreover, it introduces a mechanism to calculate the maximum achievable similarity score for each conversion. In addition, it offers crucial insights for both saliency map evaluation and the training of machine learning systems dedicated to saliency map generation. Experimental results demonstrate the method's efficacy in producing saliency maps that align seamlessly with diverse evaluation metrics, showcasing its adaptability and predictive capabilities. This approach con-tributes not only to the refinement of saliency map generation but also to the broader understanding of the intricacies involved in converting eye tracker data into meaningful ground truths. © 2024 IEEE.
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| 2. |
- Dahl, Mattias, et al.
(author)
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Analytical Modeling for a Video-Based Vehicle Speed Measurement Framework
- 2020
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In: Sensors. - : MDPI. - 1424-8220. ; 20:1
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Journal article (peer-reviewed)abstract
- Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera framerate as well as the placement and number of intrusion lines in advance. This analytical modelis verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition,as aproof-of-concept, the proposed model is implemented for avideo-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.
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| 3. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Adjustable Contrast Enhancement Using Fast Piecewise Linear Histogram Equalization
- 2020
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In: PROCEEDINGS OF THE 2020 3RD INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS PROCESSING (ICIGP 2020). - New York, NY, USA : Association for Computing Machinery. - 9781450377201 ; , s. 57-61
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Conference paper (peer-reviewed)abstract
- Histogram equalization is a technique to enhance the contrast of the image by redistributing the histogram. In this paper, a fast piecewise linear histogram equalization method is introduced based on an adjustable degree of enhancement and piecewise continuous transformation functions using frequencies of different grey-levels. This method aims to address and maximize the contrast enhancement of the image by stretching the entire spectrum. For this purpose, particular nodes (bins) on the histogram are simultaneously detected that in comparison with recursive methods, it requires less computational time. Then, the particular nodes are stretched using transformation functions to align with the reference nodes. The experimental results indicate that the performance of the proposed method is promising in terms of contrast enhancement. Moreover, this method preserves the texture of various regions in the image very well through the equalization process by using the degree of enhancement. © 2020 Owner/Author.
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| 4. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Change detection in aerial images using a Kendall's TAU distance pattern correlation
- 2016
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In: PROCEEDINGS OF THE 2016 6TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP). - : IEEE. - 9781509027811
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Conference paper (peer-reviewed)abstract
- 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.
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| 5. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Change detection in aerial images using three-dimensional feature maps
- 2020
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In: Remote Sensing. - : MDPI. - 2072-4292. ; 12:9
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Journal article (peer-reviewed)abstract
- Interest in aerial image analysis has increased owing to recent developments in and availabilityofaerialimagingtechnologies,likeunmannedaerialvehicles(UAVs),aswellasagrowing need for autonomous surveillance systems. Variant illumination, intensity noise, and different viewpointsareamongthemainchallengestoovercomeinordertodeterminechangesinaerialimages. In this paper, we present a robust method for change detection in aerial images. To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time. In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’sheightinformation,areinvestigatedthroughamathematicalmodel. Toexhibititsapplicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising. The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.
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| 6. |
- Javadi, Mohammad Saleh, 1986-
(author)
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Computer Vision Algorithms for Intelligent Transportation Systems Applications
- 2018
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Licentiate thesis (other academic/artistic)abstract
- In recent years, Intelligent Transportation Systems (ITS) have emerged asan efficient way of enhancing traffic flow, safety and management. Thesegoals are realized by combining various technologies and analyzing the acquireddata from vehicles and roadways. Among all ITS technologies, computervision solutions have the advantages of high flexibility, easy maintenanceand high price-performance ratio that make them very popular fortransportation surveillance systems. However, computer vision solutionsare demanding and challenging due to computational complexity, reliability,efficiency and accuracy among other aspects. In this thesis, three transportation surveillance systems based on computervision are presented. These systems are able to interpret the imagedata and extract the information about the presence, speed and class ofvehicles, respectively. The image data in these proposed systems are acquiredusing Unmanned Aerial Vehicle (UAV) as a non-stationary sourceand roadside camera as a stationary source. The goal of these works is toenhance the general performance of accuracy and robustness of the systemswith variant illumination and traffic conditions. This is a compilation thesis in systems engineering consisting of threeparts. The red thread through each part is a transportation surveillancesystem. The first part presents a change detection system using aerial imagesof a cargo port. The extracted information shows how the space isutilized at various times aiming for further management and developmentof the port. The proposed solution can be used at different viewpoints andillumination levels e.g. at sunset. The method is able to transform the imagestaken from different viewpoints and match them together. Thereafter,it detects discrepancies between the images using a proposed adaptive localthreshold. In the second part, a video-based vehicle's speed estimationsystem is presented. The measured speeds are essential information for lawenforcement and they also provide an estimation of traffic flow at certainpoints on the road. The system employs several intrusion lines to extractthe movement pattern of each vehicle (non-equidistant sampling) as an inputfeature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are alsotaken into account to address the uncertainty in the measurements and toobtain the probability density function of the vehicle's speed. In the thirdpart, a vehicle classification system is provided to categorize vehicles into\private car", \light trailer", \lorry or bus" and \heavy trailer". This informationcan be used by authorities for surveillance and development ofthe roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. Thesystem has been constructed by using prior knowledge of traffic regulationsregarding each class of vehicle in order to enhance the classification performance.
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| 7. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Design of a video-based vehicle speed measurement system : an uncertainty approach
- 2018
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In: <em>2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)</em>, Kitakyushu, Japan, 2018, pp. 44-49.. - : IEEE. - 9781538651612
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Conference paper (peer-reviewed)abstract
- 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.
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| 8. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features
- 2018
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In: Procedia Computer Science. - : Elsevier. ; , s. 1344-1350
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Conference paper (peer-reviewed)abstract
- 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.
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| 9. |
- Javadi, Mohammad Saleh, 1986-, et al.
(author)
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Vehicle speed measurement model for video-based systems
- 2019
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In: Computers & electrical engineering. - : Elsevier. - 0045-7906 .- 1879-0755. ; 76, s. 238-248
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Journal article (peer-reviewed)abstract
- 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.
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| 10. |
- Javadi, Saleh, 1986-
(author)
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Computer Vision for Traffic Surveillance Systems : Methods and Applications
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Computer vision solutions play a significant role in intelligent transportation systems (ITS) by improving traffic flow, safety and management. In addition, they feature prominently in autonomous vehicles and their future development. The main advantages of vision-based systems are their flexibility, coverage and accessibility. Moreover, computational power and recent algorithmic advances have increased the promise of computer vision solutions and broadened their implementation. However, computational complexity, reliability and efficiency remain among the challenges facing vision-based systems.Most traffic surveillance systems in ITS comprise three major criteria: vehicle detection, tracking and classification. In this thesis, computer vision systems are introduced to accomplish goals corresponding to these three criteria: 1) to detect the changed regions of an industrial harbour's parking lot using aerial images, 2) to estimate the speed of the vehicles on the road using a stationary roadside camera and 3) to classify vehicles using a stationary roadside camera and aerial images.The first part of this thesis discusses change detection in aerial images, which is the core of many remote sensing applications. The aerial images were taken over an industrial harbour using unmanned aerial vehicles on different days and under various circumstances. This thesis presents two approaches to detecting changed regions: a local pattern descriptor and three-dimensional feature maps. These methods are robust to varying illumination and shadows. Later, the introduced 3D feature map generation model was employed for vehicle detection in aerial images.The second part of this thesis deals with vehicle speed estimation using roadside cameras. Information regarding the flow, speed and number of vehicles is essential for traffic surveillance systems. In this thesis, two vision-based vehicle speed estimation approaches are proposed. These analytical models consider the measurement uncertainties related to the camera sampling time. The main contribution of these models is to estimate a speed probability density function for every vehicle. Later, the speed estimation model was utilised for vehicle classification using a roadside camera.Finally, in the third part, two vehicle classification models are proposed for roadside and aerial images. The first model utilises the proposed speed estimation method to extract the speed of the passing vehicles. Then, we used a fuzzy c-means algorithm to classify vehicles using their speeds and dimension features. The results show that vehicle speed is a useful feature for distinguishing different categories of vehicles. The second model employs deep neural networks to detect and classify heavy vehicles in aerial images. In addition, the proposed 3D feature generation model was utilised to improve the performance of the deep neural network. The experimental results show that 3D feature information can significantly reduce false positives in the deep learning model's output.This thesis comprises two chapters: Introduction, and Publications. In the introduction section, we discuss the motivation for computer vision solutions and their importance. Furthermore, the concepts and algorithms used to construct the proposed methods are explained. The second chapter presents the included publications.
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