Browsing NTNU Open by Author "Ullah, Mohib"

Now showing items 1-10 of 10

    • 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 ...