BlindSpotNet: Seeing Where We Cannot See
Taichi Fukuda, Kotaro Hasegawa, Shinya Ishizaki, Shohei Nobuhara, and, Ko Nishino

TL;DR
BlindSpotNet is a novel approach that estimates road blind spots from monocular images to enhance road scene understanding and safety, using a large-scale automatically generated dataset and a dedicated neural network.
Contribution
We propose a new method for 2D blind spot estimation from monocular cameras, including automatic dataset creation and a neural network model, addressing challenges of 3D reasoning and real-time detection.
Findings
The RBS dataset effectively trains blind spot estimation models.
BlindSpotNet accurately predicts blind spots in diverse driving scenarios.
Our approach outperforms existing methods in blind spot detection accuracy.
Abstract
We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents (e.g., draw attention to a road region from which a child may spring out). Detecting blind spots in full 3D would be challenging, as 3D reasoning on the fly even if the car is equipped with LiDAR would be prohibitively expensive and error prone. We instead propose to learn to estimate blind spots in 2D, just from a monocular camera. We achieve this in two steps. We first introduce an automatic method for generating ``ground-truth'' blind spot training data for arbitrary driving videos by leveraging monocular depth estimation, semantic segmentation, and SLAM. The key idea is to reason in 3D but from 2D…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
