Nitin Nilesh

👋 Hey, I am Nitin Nilesh, currently employed as a Camera Modeling Engineer at Qualcomm, where my primary focus is on the development and enhancement of Image Signal Processor pipelines. My educational background includes a Master of Science by Research degree from the International Institute of Information Technology, Hyderabad (IIIT Hyderabad). During my time at IIIT Hyderabad, I conducted research at the intersection of Computer Vision, Machine Learning, Deep Learning, and IoT, under the guidance of Prof. Sachin Chaudhari and Prof. C.V. Jawahar.

Prior to my role at Qualcomm, I had the privilege of interning with the Amazon India Machine Learning team. During this internship, I had the opportunity to collaborate with exceptional individuals and worked on projects involving Graph Neural Networks.

My research interests encompass various domains, including Computer Vision, Computational Photography, Applied Machine Learning and Deep Learning, and the Internet of Things (IoT).

Aside from my research endeavors, I have a passion for education and mentoring. I actively served as a mentor at TalentSprint, where I contributed to the AI/ML foundations course offered in collaboration between IIIT Hyderabad and Talent Sprint. In this role, I delivered lectures on fundamental concepts in Machine Learning and Deep Learning to industry professionals, guiding them through their project work.

Apart from this, I am an enthusiast of sports, particularly Badminton and Chess. Additionally, I enjoy engaging in cycling and running as part of my recreational pursuits.

Updates

Jul 8, 2023	Defended my Master’s thesis titled “Learning Methods for IoT: Use Cases of Air Pollution Monitoring”.
Nov 1, 2022	Presented two Papers at WF-IoT 2022 titled IoT-based AQI Estimation using Image Processing and Learning Methods and IoT and ML-based AQI Estimation using Real-time Traffic Data 🗞️
Sep 27, 2021	Joined Qualcomm as Camera Modeling Engineer!

Selected Publications

2023

Towards Real-Time Analysis of Broadcast Badminton Videos

Nitin Nilesh, Tushar Sharma, Anurag Ghosh, and C.V. Jawahar

Arxiv Preprint, Oct 2023

An end-to-end framework for real-time badminton analysis, tested live in Premier Badminton League (PBL). Find the live broadcast by Hotstar showing the analysis here.

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Analysis of player movements is a crucial subset of sports analysis. Existing player movement analysis methods use recorded videos after the match is over. In this work, we propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos. We only use the visual inputs from the match and, unlike other approaches which use multi-modal sensor data, our approach uses only visual cues. We propose a method to calculate the on-court distance covered by both the players from the video feed of a live broadcast badminton match. To perform this analysis, we focus on the gameplay by removing replays and other redundant parts of the broadcast match. We then perform player tracking to identify and track the movements of both players in each frame. Finally, we calculate the distance covered by each player and the average speed with which they move on the court. We further show a heatmap of the areas covered by the player on the court which is useful for analyzing the gameplay of the player. Our proposed framework was successfully used to analyze live broadcast matches in real-time during the Premier Badminton League 2019 (PBL 2019), with commentators and broadcasters appreciating the utility.

2022

IoT-based AQI Estimation using Image Processing and Learning Methods

Nitin Nilesh, Ishan Patwardhan, Jayati Narang, and Sachin Chaudhari

IEEE 8th World Forum on Internet of Things (WF-IoT), Oct 2022

Winner of the Environmental Sensing Project Competition 2022.

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Air pollution is a concern to the health of all living beings. It is essential to check on the quality of air in the surroundings. This article presents an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors on Indian roads. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) method along with image processing techniques were used to extract image features while an Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction.
IoT and ML-based AQI Estimation using Real-time Traffic Data

Nitin Nilesh, Jayati Narang, Ayu Parmar, and Sachin Chaudhari

IEEE 8th World Forum on Internet of Things (WF-IoT), Oct 2022

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This paper proposes an IoT and machine learning (ML)-based novel method to estimate the air quality index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different ML models, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), are trained on this dataset to predict the AQI category into five levels. The experimental results show an accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models were also trained on individual node datasets, and the behavior of AQI levels was observed.