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- 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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| 2. |
- Alves, Dimas irion, et al.
(author)
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Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach
- 2023
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In: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743
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Journal article (peer-reviewed)abstract
- This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
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| 3. |
- Ivanenko, Yevhen, et al.
(author)
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Phase Control in Interpolation for Backprojection of THz FMCW SAR Signals
- 2022
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In: 2022 23rd International Radar Symposium (IRS). - : IEEE. - 9788395602054 ; , s. 10-15
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Conference paper (peer-reviewed)abstract
- The THz frequency spectrum opens a lot of applications in the imaging at sub-mm level. The increase of the operating frequency band for SAR imaging systems to the THz range has proportionally affected the amount of raw data to be stored and used for accurate image reconstruction. As a consequence, improvements in the existing SAR imaging algorithms to reduce the amount of data needed to achieve the appropriate quality of imaging is desired. This paper introduces the phase control procedure as an extension to the existing sinc interpolator for backprojecting complex-valued FMCW SAR data into a defined image plane. The proposed extension controls the phase of interpolated complex-valued SAR data parameters so that it includes appropriate information about the range distance between the SAR system and the given point of space. The extended algorithm is incorporated into the global backprojection algorithm and examined on the measurement data acquired via the 2pSENSE FMCW SAR system. The efficiency of the extended algorithm is evaluated through the comparison with the conventional nearest neighbor and sinc interpolation algorithms. © 2022 Warsaw University of Technology.
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| 4. |
- Rameez, Muhammad, 1988-
(author)
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Signal Processing Approaches for Interference Mitigation in Automotive Radar Systems
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Modern vehicles have several autonomous and semi-autonomous features, such as adaptive cruise control, lane keeping, adaptive headlights, and automatic emergency braking, ensuring a safe and comfortable driving experience. The vehicles typically rely on different sensors to "see" their surroundings and make decisions accordingly. Among these sensors, radar is particularly significant for its exceptional range and velocity estimation capabilities and plays an essential role in detecting and tracking objects within the vehicle's vicinity.Since automotive radars operate in the same frequency range, there is a chance that radars operating in close proximity might encounter mutual interference. The interference can degrade the radar's performance and cause false alarms and missed detections, which can be particularly problematic in safety-oriented systems. This research aims to develop signal processing techniques to mitigate the interference effects in frequency-modulated continuous wave (FMCW) radars operating at 77-81 GHz and contribute to making modern radar applications safe and reliable. The interference mitigation methods investigated in this thesis fall into three categories: digital beamforming, time-domain signal reconstruction, and deep learning methods.The digital beamforming approach utilizes the beam pattern of the receiving antenna array to mitigate interference by placing notches in the beam pattern. It is demonstrated that while this approach is applicable to MIMO radar systems, the notch resolution does not benefit from the extended virtual aperture. An adaptive digital beamforming approach based on the least mean squares (LMS) algorithm is also proposed to suppress interference in the received signal.The time-domain signal reconstruction approaches aim to reconstruct the parts of the received baseband signal that is corrupted by the interference. It is shown that the signal coherence in the slow-time domain can be utilized to perform signal reconstruction in the slow-time. Moreover, it is shown that by compressing the interference in the time domain using pulse compression, the duration of the interference can be shortened, and an improvement in signal reconstruction performance can be achieved.Given the complexity of the mutual interference problem, deep learning-based approaches can be instrumental in interference mitigation. This research also investigates the use of deep neural network architectures such as recurrent neural networks, convolutional neural networks, and convolutional autoencoders for signal reconstruction and denoising performance.
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