MORE | Spring 2022

Monocular 3D Object Detection for Traffic Analysis

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Recognizing and localizing objects in the 3D space is crucial for a more accurate representation of the environment for various use cases. While significant progress has been achieved with expensive LIDAR systems, 3D object detection is a challenging task given only a single image (without depth information). The research aims to implement a deep learning network that predicts 3D bounding boxes from Monocular images. The system will integrate into a resource-constrained traffic surveillance camera to solve tasks, such as road safety evaluation, trajectory estimation, object speed calculation, data archiving, 3D scene reconstructions, etc.

Student researcher

Himanshu Pahadia

Himanshu Pahadia

Computer science

Hometown: New Delhi, Delhi, India

Graduation date: Spring 2023

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