Punarjay Chakravarty

1.3k total citations · 1 hit paper
23 papers, 668 citations indexed

About

Punarjay Chakravarty is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Punarjay Chakravarty has authored 23 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 10 papers in Aerospace Engineering and 9 papers in Artificial Intelligence. Recurrent topics in Punarjay Chakravarty's work include Video Surveillance and Tracking Methods (9 papers), Robotics and Sensor-Based Localization (7 papers) and Advanced Vision and Imaging (5 papers). Punarjay Chakravarty is often cited by papers focused on Video Surveillance and Tracking Methods (9 papers), Robotics and Sensor-Based Localization (7 papers) and Advanced Vision and Imaging (5 papers). Punarjay Chakravarty collaborates with scholars based in United States, Belgium and Australia. Punarjay Chakravarty's co-authors include Tinne Tuytelaars, Rahaf Aljundi, Boris Ivanovic, Ray Jarvis, Marco Pavone, Daniel Morris, Y. F. Long, Xiaoming Liu, Tim Salzmann and Hugo Van hamme and has published in prestigious journals such as IEEE Transactions on Cybernetics, IEEE Robotics and Automation Letters and IEEE Transactions on Artificial Intelligence.

In The Last Decade

Punarjay Chakravarty

21 papers receiving 651 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Expert Gate: Lifelong Learning with a Network of Experts

2017 323 citations Rahaf Aljundi, Punarjay Chakravarty et al. Lirias (KU Leuven) profile →

Peers

Punarjay Chakravarty

CVPR

AI

AE

SP

Vijay Badrinarayanan United Kingdom

Peixi Peng China

Adams Wei Yu United States

Gang Zhang China

Lingli Yu China

Antonino Furnari Italy

Alexandrina Rogozan France

Abdulwahab Alazeb Saudi Arabia

Daniel Ponsa Spain

Mengye Ren Canada

Vijay Badrinarayanan United Kingdom

Punarjay Chakravarty

666 ×1.7

CVPR

189 ×0.5

AI

146 ×1.1

AE

68 ×0.8

AE

25 ×0.4

SP

Citations per year, relative to Punarjay Chakravarty Punarjay Chakravarty (= 1×) peers Vijay Badrinarayanan

Countries citing papers authored by Punarjay Chakravarty

Since Specialization

Citations

This map shows the geographic impact of Punarjay Chakravarty'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 Punarjay Chakravarty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Punarjay Chakravarty more than expected).

Fields of papers citing papers by Punarjay Chakravarty

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Punarjay Chakravarty. 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 Punarjay Chakravarty. The network helps show where Punarjay Chakravarty may publish in the future.

Co-authorship network of co-authors of Punarjay Chakravarty

This figure shows the co-authorship network connecting the top 25 collaborators of Punarjay Chakravarty. A scholar is included among the top collaborators of Punarjay Chakravarty 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 Punarjay Chakravarty. Punarjay Chakravarty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Chakravarty, Punarjay, et al.. (2023). Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture. IEEE Transactions on Artificial Intelligence. 5(2). 904–915.

2.

Long, Y. F., Abhinav Kumar, Daniel Morris, et al.. (2023). RADIANT: Radar-Image Association Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 37(2). 1808–1816. 23 indexed citations

3.

Garg, Sourav, et al.. (2023). DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions. 2 indexed citations

4.

Ivanovic, Boris, et al.. (2022). Propagating State Uncertainty Through Trajectory Forecasting. 2022 International Conference on Robotics and Automation (ICRA). 2351–2358. 14 indexed citations

5.

Garg, Sourav, et al.. (2022). Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems. IEEE Robotics and Automation Letters. 7(4). 10112–10119. 6 indexed citations

6.

Zou, Zhengxia, Rusheng Zhang, Shengyin Shen, et al.. (2022). Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras. 2022 International Conference on Robotics and Automation (ICRA). 890–896. 23 indexed citations

7.

Chakravarty, Punarjay, et al.. (2021). Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian Optimization. IEEE Transactions on Cybernetics. 53(4). 2261–2274. 6 indexed citations

8.

Long, Y. F., et al.. (2021). Radar-Camera Pixel Depth Association for Depth Completion. 12502–12511. 59 indexed citations

9.

Chakravarty, Punarjay, et al.. (2021). Can We Localize an Autonomous Vehicle From a Single Image? Deep-Geometric Six Degrees-of-Freedom Localization in Topo-Metric Maps. 1(3).

10.

Chakravarty, Punarjay, et al.. (2020). CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection. 1 indexed citations

11.

Chakravarty, Punarjay, et al.. (2017). CNN-based single image obstacle avoidance on a quadrotor. Lirias (KU Leuven). 6369–6374. 51 indexed citations

12.

Aljundi, Rahaf, Punarjay Chakravarty, & Tinne Tuytelaars. (2017). Expert Gate: Lifelong Learning with a Network of Experts. Lirias (KU Leuven). 7120–7129. 323 indexed citations breakdown →

13.

Chakravarty, Punarjay, et al.. (2016). Active speaker detection with audio-visual co-training. Lirias (KU Leuven). 312–316. 16 indexed citations

14.

Chakravarty, Punarjay, et al.. (2015). Who's Speaking?. Lirias (KU Leuven). 87–90. 24 indexed citations

15.

Chakravarty, Punarjay, David Rawlinson, & Ray Jarvis. (2011). Covert Behaviour Detection in a Collaborative Surveillance System. 1 indexed citations

16.

Chakravarty, Punarjay, et al.. (2009). External cameras and a mobile robot: A collaborative surveillance system. 1–10. 6 indexed citations

17.

Chakravarty, Punarjay, et al.. (2008). People tracking from a moving panoramic camera. 1–10. 2 indexed citations

18.

Chakravarty, Punarjay & Ray Jarvis. (2006). Panoramic Vision and Laser Range Finder Fusion for Multiple Person Tracking. 2949–2954. 31 indexed citations

19.

Chakravarty, Punarjay, et al.. (2006). Person tracking, pursuit & interception by mobile robot. 1–7. 4 indexed citations

20.

Chakravarty, Punarjay & R.A. Jarvis. (2005). Multiple Target Tracking for Surveillance: A Particle Filter Approach. 181–186. 7 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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