Mohamed Abdelwahab | Aswan University (original) (raw)
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Papers by Mohamed Abdelwahab
IEEE Access, 2020
Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of... more Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of vehicles. Existing systems largely depend on texture and motion features. Such systems face several challenges, including illumination changes caused by variations in weather conditions, complexity of scenes, vehicle occlusion, and the ambiguity of stopped vehicles. To overcome these issues, this article proposes a rapid and reliable traffic congestion detection method based on the modeling of video dynamics using deep residual learning and motion trajectories. The proposed method efficiently uses both motion and deep texture features to overcome the limitations of existing methods. Unlike other methods that simply extract texture features from a single frame, we use an efficient representation learning method to capture the latent structures in traffic videos by modeling the evolution of texture features. This representation yields a noticeable improvement in detection results under various weather conditions. Regarding motion features, we propose an algorithm to distinguish stopped vehicles and background objects, whereas most existing motion-based approaches fail to address this issue. Both types of obtained features are used to construct an ensemble classification model based on the support vector machine algorithm. Two benchmark datasets are considered to demonstrate the robustness of the proposed method: the UCSD dataset and NU1 video dataset. The proposed method achieves competitive results (97.64% accuracy) when compared to state-ofthe-art methods.
IEEE Access, 2021
The analysis of the movement of people in a shopping area with the aim of improving marketing is ... more The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call ''Nigiwai,'' from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.
Electronics Letters, 2018
Systems for counting vehicles should be fast enough to be implemented in real-time situations. Mo... more Systems for counting vehicles should be fast enough to be implemented in real-time situations. Most of the related work uses two stages for vehicle counting, vehicle detection and tracking, which increase the computational complexity. In this Letter, a fast and efficient approach for vehicle counting is proposed, where there is no need for the vehicle tracking step. A background model is created only for a narrow region, a line, in the video frames. The moving vehicles are detected as foreground objects while passing this narrow region. Morphological processes are applied to the extracted objects to enhance them and decrease the effects of vehicle occlusions. Finally, an efficient counting vehicles method is introduced employing only the extracted detection information. The experimental results performed on diverse videos show that the proposed method is fast and accurate. The average execution time per frame is 7.78 ms.
SVU-International Journal of Engineering Sciences and Applications
Cities with high population density have a serious problem with traffic congestion. Intelligent t... more Cities with high population density have a serious problem with traffic congestion. Intelligent transportation systems try to overcome these problems by finding smart ways to detect traffic congestion. One of the essential issues in these systems is selecting the appropriate features to detect traffic congestion. Most of the current methods utilize motion or texture features only, which have their limitations. In this paper, a deep neural network (DNN), which has two input paths, is proposed for traffic congestion recognition. It handles the evolution of motion as well as texture through its two inputs simultaneously via Long Short-Term Memory (LSTM) layers. Gaussian noise layers are used to increase the generalization ability of the DNN and to enable training on small datasets without over-fitting. Experimental results applied to the UCSD and NU videos datasets assert the robustness of the proposed method. It achieves an accuracy of 98 % which is high in comparison to the state-of-the-art methods.
Applying vector quantization (VQ) to an image delivers an index map. Dividing this index map into... more Applying vector quantization (VQ) to an image delivers an index map. Dividing this index map into blocks results in each block using a limited number of codeword indices of the whole codebook (CB) while the rest of indices are unused. The proposed method exploits the unused indices to embed the watermark bits. The proposed watermark gives a high peak signal to noise ratio (PSNR) while keeping bit per pixel (bpp) at a small value, so it can be easily used in transmitting digital images over the internet. Furthermore, the reconstructed image has robustness against different attacks such as cropping, JPEG compression, and median filter. So it can be used in copyright protection.
IEEE Access, 2020
Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of... more Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of vehicles. Existing systems largely depend on texture and motion features. Such systems face several challenges, including illumination changes caused by variations in weather conditions, complexity of scenes, vehicle occlusion, and the ambiguity of stopped vehicles. To overcome these issues, this article proposes a rapid and reliable traffic congestion detection method based on the modeling of video dynamics using deep residual learning and motion trajectories. The proposed method efficiently uses both motion and deep texture features to overcome the limitations of existing methods. Unlike other methods that simply extract texture features from a single frame, we use an efficient representation learning method to capture the latent structures in traffic videos by modeling the evolution of texture features. This representation yields a noticeable improvement in detection results under various weather conditions. Regarding motion features, we propose an algorithm to distinguish stopped vehicles and background objects, whereas most existing motion-based approaches fail to address this issue. Both types of obtained features are used to construct an ensemble classification model based on the support vector machine algorithm. Two benchmark datasets are considered to demonstrate the robustness of the proposed method: the UCSD dataset and NU1 video dataset. The proposed method achieves competitive results (97.64% accuracy) when compared to state-ofthe-art methods.
IEEE Access, 2021
The analysis of the movement of people in a shopping area with the aim of improving marketing is ... more The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call ''Nigiwai,'' from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.
Electronics Letters, 2018
Systems for counting vehicles should be fast enough to be implemented in real-time situations. Mo... more Systems for counting vehicles should be fast enough to be implemented in real-time situations. Most of the related work uses two stages for vehicle counting, vehicle detection and tracking, which increase the computational complexity. In this Letter, a fast and efficient approach for vehicle counting is proposed, where there is no need for the vehicle tracking step. A background model is created only for a narrow region, a line, in the video frames. The moving vehicles are detected as foreground objects while passing this narrow region. Morphological processes are applied to the extracted objects to enhance them and decrease the effects of vehicle occlusions. Finally, an efficient counting vehicles method is introduced employing only the extracted detection information. The experimental results performed on diverse videos show that the proposed method is fast and accurate. The average execution time per frame is 7.78 ms.
SVU-International Journal of Engineering Sciences and Applications
Cities with high population density have a serious problem with traffic congestion. Intelligent t... more Cities with high population density have a serious problem with traffic congestion. Intelligent transportation systems try to overcome these problems by finding smart ways to detect traffic congestion. One of the essential issues in these systems is selecting the appropriate features to detect traffic congestion. Most of the current methods utilize motion or texture features only, which have their limitations. In this paper, a deep neural network (DNN), which has two input paths, is proposed for traffic congestion recognition. It handles the evolution of motion as well as texture through its two inputs simultaneously via Long Short-Term Memory (LSTM) layers. Gaussian noise layers are used to increase the generalization ability of the DNN and to enable training on small datasets without over-fitting. Experimental results applied to the UCSD and NU videos datasets assert the robustness of the proposed method. It achieves an accuracy of 98 % which is high in comparison to the state-of-the-art methods.
Applying vector quantization (VQ) to an image delivers an index map. Dividing this index map into... more Applying vector quantization (VQ) to an image delivers an index map. Dividing this index map into blocks results in each block using a limited number of codeword indices of the whole codebook (CB) while the rest of indices are unused. The proposed method exploits the unused indices to embed the watermark bits. The proposed watermark gives a high peak signal to noise ratio (PSNR) while keeping bit per pixel (bpp) at a small value, so it can be easily used in transmitting digital images over the internet. Furthermore, the reconstructed image has robustness against different attacks such as cropping, JPEG compression, and median filter. So it can be used in copyright protection.