Farzeen Munir

786 total citations
29 papers, 449 citations indexed

About

Farzeen Munir is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Aerospace Engineering. According to data from OpenAlex, Farzeen Munir has authored 29 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 12 papers in Automotive Engineering and 9 papers in Aerospace Engineering. Recurrent topics in Farzeen Munir's work include Autonomous Vehicle Technology and Safety (11 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (7 papers). Farzeen Munir is often cited by papers focused on Autonomous Vehicle Technology and Safety (11 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (7 papers). Farzeen Munir collaborates with scholars based in South Korea, Pakistan and Finland. Farzeen Munir's co-authors include Moongu Jeon, Shoaib Azam, Witold Pedrycz, Yeongmin Ko, Younkwan Lee, Ahmad Muqeem Sheri, Kin‐Choong Yow, Byung‐Geun Lee, Fayyaz Minhas and Amina Asif and has published in prestigious journals such as IEEE Access, Sensors and Information Sciences.

In The Last Decade

Farzeen Munir

28 papers receiving 434 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Farzeen Munir South Korea 10 291 257 83 60 59 29 449
Shoaib Azam South Korea 11 310 1.1× 263 1.0× 87 1.0× 63 1.1× 67 1.1× 30 466
Mirko Meuter Germany 10 248 0.9× 194 0.8× 59 0.7× 51 0.8× 53 0.9× 26 350
Hengli Wang Hong Kong 11 359 1.2× 146 0.6× 97 1.2× 65 1.1× 61 1.0× 23 570
Stefan Müller-Schneiders Germany 13 300 1.0× 168 0.7× 60 0.7× 64 1.1× 64 1.1× 25 385
Morten B. Jensen Denmark 9 269 0.9× 125 0.5× 44 0.5× 43 0.7× 48 0.8× 17 426
Yifeng Shi China 10 388 1.3× 177 0.7× 78 0.9× 31 0.5× 127 2.2× 18 575
Marvin Teichmann United Kingdom 2 413 1.4× 149 0.6× 106 1.3× 38 0.6× 59 1.0× 2 513
Shih-Shinh Huang Taiwan 11 425 1.5× 264 1.0× 65 0.8× 59 1.0× 26 0.4× 38 543
Zhengkai Tu China 9 410 1.4× 293 1.1× 154 1.9× 82 1.4× 37 0.6× 16 555
Suree Pumrin Thailand 10 270 0.9× 118 0.5× 51 0.6× 46 0.8× 23 0.4× 34 402

Countries citing papers authored by Farzeen Munir

Since Specialization
Citations

This map shows the geographic impact of Farzeen Munir's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Farzeen Munir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farzeen Munir more than expected).

Fields of papers citing papers by Farzeen Munir

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Farzeen Munir. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Farzeen Munir. The network helps show where Farzeen Munir may publish in the future.

Co-authorship network of co-authors of Farzeen Munir

This figure shows the co-authorship network connecting the top 25 collaborators of Farzeen Munir. A scholar is included among the top collaborators of Farzeen Munir based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Farzeen Munir. Farzeen Munir is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Munir, Farzeen, et al.. (2025). Pedestrian Vision Language Model for Intentions Prediction. IEEE Open Journal of Intelligent Transportation Systems. 6. 393–406. 3 indexed citations
2.
Munir, Farzeen & Tomasz Piotr Kucner. (2025). Context-aware multi-task learning for pedestrian intent and trajectory prediction. Transportation Research Part C Emerging Technologies. 178. 105203–105203.
3.
Azam, Shoaib, Farzeen Munir, Ville Kyrki, et al.. (2024). Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving. Engineering Applications of Artificial Intelligence. 135. 108767–108767. 3 indexed citations
4.
Munir, Farzeen, et al.. (2024). Exploring Large Language Models for Trajectory Prediction: A Technical Perspective. Aaltodoc (Aalto University). 774–778. 2 indexed citations
5.
Munir, Farzeen, Shoaib Azam, Kin‐Choong Yow, Byung‐Geun Lee, & Moongu Jeon. (2023). Multimodal fusion for sensorimotor control in steering angle prediction. Engineering Applications of Artificial Intelligence. 126. 107087–107087. 6 indexed citations
6.
Munir, Farzeen, Shoaib Azam, Tomasz Piotr Kucner, Ville Kyrki, & Moongu Jeon. (2023). Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks. Aaltodoc (Aalto University). 3575–3582. 1 indexed citations
7.
Munir, Farzeen, et al.. (2022). An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram. IEEE Access. 10. 99050–99065. 3 indexed citations
8.
Munir, Farzeen, et al.. (2022). Exploring thermal images for object detection in underexposure regions for autonomous driving. Applied Soft Computing. 121. 108793–108793. 30 indexed citations
9.
Munir, Farzeen, Shoaib Azam, Moongu Jeon, Byung‐Geun Lee, & Witold Pedrycz. (2021). LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor. IEEE Transactions on Intelligent Transportation Systems. 23(7). 9318–9334. 18 indexed citations
10.
Azam, Shoaib, et al.. (2021). N 2 C: Neural Network Controller Design Using Behavioral Cloning. IEEE Transactions on Intelligent Transportation Systems. 22(7). 4744–4756. 10 indexed citations
11.
Ko, Yeongmin, Younkwan Lee, Shoaib Azam, et al.. (2021). Key Points Estimation and Point Instance Segmentation Approach for Lane Detection. IEEE Transactions on Intelligent Transportation Systems. 23(7). 8949–8958. 199 indexed citations
12.
Munir, Farzeen, Shoaib Azam, & Moongu Jeon. (2021). SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 206–213. 34 indexed citations
13.
Azam, Shoaib, et al.. (2020). Multiple Objects Tracking using Radar for Autonomous Driving. 1–4. 15 indexed citations
14.
Azam, Shoaib, Farzeen Munir, & Moongu Jeon. (2020). Dynamic Control System Design for Autonomous Car. 456–463. 3 indexed citations
15.
Munir, Farzeen, Shoaib Azam, & Moongu Jeon. (2020). Visuomotor Steering angle Prediction in Dynamic Perception Environment for Autonomous Vehicle. 1–6. 2 indexed citations
16.
Azam, Shoaib, et al.. (2019). Data fusion of Lidar and Thermal Camera for Autonomous driving. T2A.5–T2A.5. 8 indexed citations
17.
Munir, Farzeen, et al.. (2019). MILAMP: Multiple Instance Prediction of Amyloid Proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(3). 1142–1150. 10 indexed citations
18.
Munir, Farzeen, et al.. (2019). Transfer learning for vehicle detection using two cameras with different focal lengths. Information Sciences. 514. 71–87. 20 indexed citations
19.
Azam, Shoaib, et al.. (2019). Automated Taxi Booking Operations for Autonomous Vehicles. 10. 1–5. 1 indexed citations
20.
Munir, Farzeen, et al.. (2018). Autonomous Vehicle. 1–5. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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