| 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 | 178 | 2017 |
| A survey of advances in vision-based vehicle re-identification SD Khan, H Ullah Computer Vision and Image Understanding, 2019 | 117 | 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 | 100 | 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 | 84 | 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 | 59 | 2016 |
| Scale Driven Convolutional Neural Network Model For People Counting and Localization in Crowd Scenes S Basalamah, SD Khan, H Ullah IEEE Access, 2019 | 51 | 2019 |
| 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 | 39 | 2013 |
| 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 | 38 | 2017 |
| 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 | 38 | 2016 |
| Generation of multiple Fano resonances in plasmonic split nanoring dimer AD Khan, SD Khan, RU Khan, N Ahmad, A Ali, A Khalil, FA Khan Plasmonics 9, 1091-1102, 2014 | 37 | 2014 |
| 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 | 36 | 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 | 35 | 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 | 34 | 2021 |
| Excitation of multiple Fano-like resonances induced by higher order plasmon modes in three-layered bimetallic nanoshell dimer AD Khan, SD Khan, RU Khan, N Ahmad Plasmonics 9, 461-475, 2014 | 34 | 2014 |
| 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 | 33 | 2019 |
| Detecting dominant motion flows and people counting in high density crowds SD Khan, G Vizzari, S Bandini, S Basalamah Václav Skala-UNION Agency, 2014 | 33 | 2014 |
| 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 | 32 | 2020 |
| Detection of social groups in pedestrian crowds using computer vision SD Khan, G Vizzari, S Bandini, S Basalamah Advanced Concepts for Intelligent Vision Systems: 16th International …, 2015 | 31 | 2015 |
| Extracting descriptive motion information from crowd scenes M Saqib, SD Khan, N Sharma, M Blumenstein 2017 International Conference on Image and Vision Computing New Zealand …, 2017 | 29 | 2017 |
| Congestion detection in pedestrian crowds using oscillation in motion trajectories SD Khan Engineering Applications of Artificial Intelligence 85, 429-443, 2019 | 28 | 2019 |
Muhammad SaqibUniversity of Technology Sydney AustraliaVerified email at uts.edu.au
