Search Results for author: Behzad Dariush

Found 12 papers, 1 papers with code

Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast

no code implementations10 Nov 2020 Srikanth Malla, Chiho Choi, Behzad Dariush

This paper considers the problem of multi-modal future trajectory forecast with ranking.

Unsupervised Domain Adaptation for Spatio-Temporal Action Localization

no code implementations19 Oct 2020 Nakul Agarwal, Yi-Ting Chen, Behzad Dariush, Ming-Hsuan Yang

Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features.

Object Detection Spatio-Temporal Action Localization +2

Recognition and 3D Localization of Pedestrian Actions from Monocular Video

no code implementations3 Aug 2020 Jun Hayakawa, Behzad Dariush

The proposed method outperforms methods using a single-stream temporal relation network based on evaluations using the JAAD public dataset.

Action Recognition

Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera

no code implementations4 May 2020 Jun Hayakawa, Behzad Dariush

The main contribution of this paper is a new framework and algorithm that integrates these three networks in order to estimate the ego-motion and surrounding vehicle state.

Optical Flow Estimation

SSP: Single Shot Future Trajectory Prediction

no code implementations13 Apr 2020 Isht Dwivedi, Srikanth Malla, Behzad Dariush, Chiho Choi

Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.

Trajectory Prediction

TITAN: Future Forecast using Action Priors

no code implementations CVPR 2020 Srikanth Malla, Behzad Dariush, Chiho Choi

In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion.

Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment

no code implementations7 Dec 2019 Behnoosh Parsa, Athma Narayanan, Behzad Dariush

In this paper, we propose a novel Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN) for online action recognition for ergonomic risk assessment that enables the use of features from all levels of the skeleton feature hierarchy.

Action Recognition

Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

no code implementations1 Oct 2019 Athma Narayanan, Avinash Siravuru, Behzad Dariush

The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams.

Autonomous Navigation General Classification +1

NEMO: Future Object Localization Using Noisy Ego Priors

no code implementations17 Sep 2019 Srikanth Malla, Isht Dwivedi, Behzad Dariush, Chiho Choi

In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.

motion prediction Object Localization

Dynamic Traffic Scene Classification with Space-Time Coherence

no code implementations29 May 2019 Athma Narayanan, Isht Dwivedi, Behzad Dariush

This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle.

Classification General Classification +1

Looking to Relations for Future Trajectory Forecast

no code implementations ICCV 2019 Chiho Choi, Behzad Dariush

Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes.

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

2 code implementations19 Sep 2018 Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, Behzad Dariush

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.

Autonomous Driving Motion Planning +1

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