Search Results for author: Pranamesh Chakraborty

Found 9 papers, 2 papers with code

The 8th AI City Challenge

no code implementations15 Apr 2024 Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Pranamesh Chakraborty, Sanjita Prajapati, Quan Kong, Norimasa Kobori, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Fady Alnajjar, Ganzorig Batnasan, Ping-Yang Chen, Jun-Wei Hsieh, Xunlei Wu, Sameer Satish Pusegaonkar, Yizhou Wang, Sujit Biswas, Rama Chellappa

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.

Dense Video Captioning

A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding

no code implementations4 Jul 2023 Pavan Kumar Sharma, Pranamesh Chakraborty

We first discuss the fundamentals related to driver gaze, involving head-mounted and remote setup based gaze estimation and the terminologies used for each of these data collection methods.

Gaze Estimation

The 7th AI City Challenge

no code implementations15 Apr 2023 Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang, Rama Chellappa

The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.

Retrieval

Thermal infrared image based vehicle detection in low-level illumination conditions using multi-level GANs

no code implementations20 Sep 2022 Shivom Bhargava, Sanjita Prajapati, Pranamesh Chakraborty

However, these models have been found to under-perform during night-time conditions compared to day-time conditions, as day-time infrared images looks different than night-time infrared images.

object-detection Object Detection

Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

no code implementations5 May 2020 Tongge Huang, Pranamesh Chakraborty, Anuj Sharma

This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset.

Decision Making Generative Adversarial Network +6

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