Probabilistic Object Detection: Strengths, Weaknesses, Opportunities

Abstract

— Deep neural networks are the de-facto standard for object detection in autonomous driving applications. However, neural networks cannot be blindly trusted even within the training data distribution, let alone outside it. This has paved way for several probabilistic object detection techniques that measure uncertainty in the outputs of an object detector. Through this position paper, we serve three main purposes. First, we briefly sketch the landscape of current methods for probabilistic object detecion. Second, we present the main shortcomings of these approaches. Finally, we present promising avenues for future research, and proof-of-concept results where applicable. Through this effort, we hope to bring the community one step closer to performing accurate, reliable, and consistent probabilistic object detection.

Publication
In AI for Autonomous Driving - ICML workshop
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Dhaivat Bhatt
Masters Student
Gunshi Gupta
Masters Student
Krishna Murthy Jatavallabhula
Krishna Murthy Jatavallabhula
PhD Candidate

My research blends robotics, computer vision, graphics, and physics with deep learning.

Liam Paull
Assistant Professor

I lead the Montreal robotics and embodied AI lab. I am affiliated with Université de Montréal, Mila, and I hold a CIFAR AI chair.

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