Research Projects
A catalogue of my publications and conferences
PUBLICATIONS
● “Human-Robot Task Handoff: A Probabilistic Modeling Approach Explored through Cooperative Drawing”
Journal of Student Research - 5/31/24 Volume 13 Issue 2 (2024) of the Journal of Student Research https://www.jsr.org/hs/index.php/path/article/view/6545
Abstract:
Recent research in human-robot interaction explores the potential for human-machine collaboration in surgical procedures, dividing tasks into manual and automatable subtasks. This paper investigates the task of handoff detection, crucial for the success of robot-assisted surgery, focusing on the creation of a synthetic dataset which can be used for training and benchmarking models for this task. We present a dataset of parabolas, simulating cooperative drawing between a human and a robot, with variations in drawing rates and added noise. The study demonstrates the applicability of HMMs in determining handoff points, laying the groundwork for future research in human-machine collaborative surgery. The dataset, along with the provided code and raw data, are provided as a resource for future research. Finally, we discuss the limitations of the dataset and suggest directions for future research, emphasizing the need for higher-dimensional and real-world datasets.
First Author and Presenter, IEEE 100th Vehicular Technology Conference, Washington DC, Oct 7-10, 2024
Abstract:
Motorcycle accidents pose significant risks, particularly when riders and passengers do not wear helmets. This study evaluates the efficacy of an advanced vision-language foundation model, OWLv2, in detecting and classifying various helmet-wearing statuses of motorcycle occupants using video data. We extend the dataset provided by the CVPR AI City Challenge and employ a cascaded model approach for detection and classification tasks, integrating OWLv2 and CNN models. The results highlight the potential of zero-shot learning to address challenges arising from incomplete and biased training datasets, demonstrating the usage of such models in detecting motorcycles, helmet usage, and occupant positions under varied conditions. We have achieved an average precision of 0.5324 for helmet detection and provided precision-recall curves detailing the detection and classification performance. Despite limitations such as low-resolution data and poor visibility, our research shows promising advancements in automated vehicle safety and traffic safety enforcement systems.
● “Evaluating Cascaded Methods of Vision-Language Models for Zero-Shot Detection and Association of Hardhats for Increased Construction Safety”
First Author and Presenter, MIT IEEE Undergraduate Research Technology Conference, Massachusetts, Oct 11-13, 2024
Abstract:
This paper evaluates the use of vision-language models (VLMs) for zero-shot detection and association of hardhats to enhance construction safety. Given the significant risk of head injuries in construction, proper enforcement of hardhat use is critical. We investigate the applicability of foundation models, specifically OWLv2, for detecting hardhats in real-world construction site images. Our contributions include the creation of a new benchmark dataset, Hardhat Safety Detection Dataset, by filtering and combining existing datasets and the development of a cascaded detection approach. Experimental results on 5,210 images demonstrate that the OWLv2 model achieves an average precision of 0.6493 for hardhat detection. We further analyze the limitations and potential improvements for real-world applications, highlighting the strengths and weaknesses of current foundation models in safety perception domains.
● Harvard Science Research Conference, Massachusetts, Oct 19 - Oct 20, 2024
● Southern California Conference for Undergraduate Research (SCCUR), California State University, San Bernardino, Nov 23, 2024
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Author and Present project titled, “Using Quantum Machine Learning to Improve Detection of Spinal Injuries, Defects, and Illnesses”
