Search Results for author: Sujitha Martin

Found 6 papers, 0 papers with code

DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts

no code implementations3 Feb 2022 Wenzhen Zhu, Negin Sokhandan, Guang Yang, Sujitha Martin, Suchitra Sathyanarayana

Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc.

Document Layout Analysis Image Segmentation +4

Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving

no code implementations23 Feb 2021 Chen Tang, Nishan Srishankar, Sujitha Martin, Masayoshi Tomizuka

Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation.

Autonomous Driving

Interaction Graphs for Object Importance Estimation in On-road Driving Videos

no code implementations12 Mar 2020 Zehua Zhang, Ashish Tawari, Sujitha Martin, David Crandall

A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions.

Autonomous Driving Decision Making +1

Context Aware Road-user Importance Estimation (iCARE)

no code implementations30 Aug 2019 Alireza Rahimpour, Sujitha Martin, Ashish Tawari, Hairong Qi

In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene.

Decision Making Self-Driving Cars

Goal-oriented Object Importance Estimation in On-road Driving Videos

no code implementations8 May 2019 Mingfei Gao, Ashish Tawari, Sujitha Martin

We propose a novel framework that incorporates both visual model and goal representation to conduct OIE.

Object

Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction

no code implementations31 Jan 2018 Sujitha Martin, Sourabh Vora, Kevan Yuen, Mohan M. Trivedi

The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode.

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