Search Results for author:
Found 18 papers, 4 papers with code
Optimal Driver Warning Generation in Dynamic Driving Environment
An optimal warning generation framework is proposed as a solution to the proposed POMDP.
Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge
In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e. g., unable to reach high places or confined to a wheelchair -- in performing common household or outdoor tasks as efficiently as possible.
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
In the induction stage, the LLM is fed with few-shot normal reference samples and then summarizes these normal patterns to induce a set of rules for detecting anomalies.
Ranked #2 on
Video Anomaly Detection
on UBnormal
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
Weakly-Supervised Action Segmentation and Unseen Error Detection in Anomalous Instructional Videos
We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos.
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
This paper considers the problem of multi-modal future trajectory forecast with ranking.
Unsupervised Domain Adaptation for Spatio-Temporal Action Localization
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.
Recognition and 3D Localization of Pedestrian Actions from Monocular Video
The proposed method outperforms methods using a single-stream temporal relation network based on evaluations using the JAAD public dataset.
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
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.
SSP: Single Shot Future Trajectory Prediction
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
TITAN: Future Forecast using Action Priors
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
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.
Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data
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.
NEMO: Future Object Localization Using Noisy Ego Priors
In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.
Dynamic Traffic Scene Classification with Space-Time Coherence
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.
Looking to Relations for Future Trajectory Forecast
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
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
