| A study on detecting drones using deep convolutional neural networks M Saqib, SD Khan, N Sharma, M Blumenstein 2017 14th IEEE international conference on advanced video and signal based …, 2017 | 231 | 2017 |
| A survey of advances in vision-based vehicle re-identification SD Khan, H Ullah Computer Vision and Image Understanding, 2019 | 189 | 2019 |
| Internal Emotion Classification Using EEG Signal with Sparse Discriminative Ensemble H Ullah, M Uzair, A Mahmood, M Ullah, SD Khan, FA Cheikh IEEE Access, 2019 | 143 | 2019 |
| Stacked lstm network for human activity recognition using smartphone data M Ullah, H Ullah, SD Khan, FA Cheikh 2019 8th European workshop on visual information processing (EUVIP), 175-180, 2019 | 135 | 2019 |
| Scale Driven Convolutional Neural Network Model For People Counting and Localization in Crowd Scenes S Basalamah, SD Khan, H Ullah IEEE Access, 2019 | 78 | 2019 |
| Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows SD Khan, S Bandini, S Basalamah, G Vizzari Neurocomputing 177, 543-563, 2016 | 71 | 2016 |
| Toward smart lockdown: a novel approach for COVID-19 hotspots prediction using a deep hybrid neural network SD Khan, L Alarabi, S Basalamah Computers 9 (4), 99, 2020 | 57 | 2020 |
| Scale and density invariant head detection deep model for crowd counting in pedestrian crowds SD Khan, S Basalamah The Visual Computer 37 (8), 2127-2137, 2021 | 52 | 2021 |
| Attention-based LSTM network for action recognition in sports M Ullah, M Mudassar Yamin, A Mohammed, S Daud Khan, H Ullah, ... Electronic Imaging 2021 (6), 302-1-302-6, 2021 | 52 | 2021 |
| Light absorption enhancement in tri-layered composite metasurface absorber for solar cell applications AD Khan, AD Khan, SD Khan, M Noman Optical Materials 84, 195-198, 2018 | 47 | 2018 |
| Disam: Density independent and scale aware model for crowd counting and localization SD Khan, H Ullah, M Uzair, M Ullah, R Ullah, FA Cheikh 2019 IEEE International Conference on Image Processing (ICIP), 4474-4478, 2019 | 44 | 2019 |
| Multi-feature-based crowd video modeling for visual event detection H Ullah, IU Islam, M Ullah, M Afaq, SD Khan, J Iqbal Multimedia Systems 27, 589-597, 2021 | 43 | 2021 |
| Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks M Saqib, SD Khan, N Sharma, M Blumenstein IEEE Access 7, 35317-35329, 2019 | 43 | 2019 |
| Texture-based feature mining for crowd density estimation: A study M Saqib, SD Khan, M Blumenstein 2016 International Conference on Image and Vision Computing New Zealand …, 2016 | 42 | 2016 |
| Counting of people in the extremely dense crowd using genetic algorithm and blobs counting M Arif, S Daud, S Basalamah IAES International Journal of Artificial Intelligence 2 (2), 51, 2013 | 41 | 2013 |
| A unified deep learning framework of multi-scale detectors for geo-spatial object detection in high-resolution satellite images SD Khan, L Alarabi, S Basalamah Arabian Journal for Science and Engineering, 1-16, 2021 | 40 | 2021 |
| Sparse to dense scale prediction for crowd couting in high density crowds SD Khan, S Basalamah Arabian Journal for Science and Engineering 46 (4), 3051-3065, 2021 | 40 | 2021 |
| An encoder–decoder deep learning framework for building footprints extraction from aerial imagery SD Khan, L Alarabi, S Basalamah Arabian Journal for Science and Engineering 48 (2), 1273-1284, 2023 | 39 | 2023 |
| Congestion detection in pedestrian crowds using oscillation in motion trajectories SD Khan Engineering Applications of Artificial Intelligence 85, 429-443, 2019 | 39 | 2019 |
| Towards a crowd analytic framework for crowd management in Majid-al-Haram SD Khan, M Tayyab, MK Amin, A Nour, A Basalamah, S Basalamah, ... arXiv preprint arXiv:1709.05952, 2017 | 39 | 2017 |
Muhammad SaqibUniversity of Technology Sydney Australiauts.edu.au üzerinde doğrulanmış e-posta adresine sahip