Browsing NTNU Open by Author "Ullah, Mohib"
Now showing items 1-10 of 10
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A hybrid social influence model for pedestrian motion segmentation
Ullah, Habib; Ullah, Mohib; Muhammad, Uzair (Journal article; Peer reviewed, 2018)A hybrid social influence model (HSIM) has been proposed which is a novel and automatic method for pedestrian motion segmentation. One of the major attractions of the HSIM is its capability to handle motion segmentation ... -
Anomalous entities detection and localization in pedestrian flows
Ullah, Habib; Altamimi, Ahmed Bder; Uzair, Muhammad; Ullah, Mohib (Journal article; Peer reviewed, 2018)We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features for efficient and robust motion ... -
Density independent hydrodynamics model for crowd coherency detection
Ullah, Habib; Uzair, Muhammad; Ullah, Mohib; Khan, Asif; Ahmad, Ayaz; Khan, Wilayat (Journal article; Peer reviewed, 2017)We propose density independent hydrodynamics model (DIHM) which is a novel and automatic method for coherency detection in crowded scenes. One of the major advantages of the DIHM is its capability to handle changing density ... -
Disam: Density Independent and Scale Aware Model for Crowd Counting and Localization
Khan, Sultan Daud; Ullah, Habib; Uzair, Mohammad; Ullah, Mohib; Ullah, Rehan; Alaya Cheikh, Faouzi (Journal article; Peer reviewed, 2019)People counting in high density crowds is emerging as a new frontier in crowd video surveillance. Crowd counting in high density crowds encounters many challenges, such as severe occlusions, few pixels per head, and large ... -
Facial Emotion Recognition Using Hybrid Features
Ullah, Mohib; Alreshidi, Abdulrahman (Peer reviewed; Journal article, 2020)Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ ... -
Human action recognition in videos using stable features
Ullah, Mohib; Ullah, Habib; Alseadonn, Ibrahim M (Journal article, 2017)Human action recognition is still a challenging problem and researchers are focusing to investigate this problem using different techniques. We propose a robust approach for human action recognition. This is achieved by ... -
Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble
Ullah, Habib; Muhammad, Uzair; Mahmood, Arif; Ullah, Mohib; Khan, Sultan Daud; Cheikh, Faouzi Alaya (Journal article; Peer reviewed, 2019)Among various physiological signal acquisition methods for the study of the human brain, EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive, and accurate way of capturing brain signals ... -
Multi-Target Tracking and Segmentation for Video Surveillance
Ullah, Mohib (Doctoral theses at NTNU;2019:128, Doctoral thesis, 2019) -
PedNet: A Spatio-Temporal Deep Convolutional Neural Network for Pedestrian Segmentation
Ullah, Mohib; Mohammed, Ahmed Kedir; Alaya Cheikh, Faouzi (Journal article; Peer reviewed, 2018)Articulation modeling, feature extraction, and classification are the important components of pedestrian segmentation. Usually, these components are modeled independently from each other and then combined in a sequential ... -
Semi-supervised Network for Detection of COVID-19 in Chest CT Scans
Mohammed, Ahmed Kedir; Wang, Congcong; Zhao, Meng; Ullah, Mohib; Naseem, Rabia; Wang, Hao; Pedersen, Marius; Alaya Cheikh, Faouzi (Peer reviewed; Journal article, 2020)Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult ...