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An exploratory study on human-centric video anomaly detection through variational autoencoders and trajectory prediction
Abstract Video Anomaly Detection (VAD) represents a challenging and prominent research
task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) …
task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) …
Vt-former: An exploratory study on vehicle trajectory prediction for highway surveillance through graph isomorphism and transformer
Enhancing roadway safety has become an essential computer vision focus area for
Intelligent Transportation Systems (ITS). As a part of ITS Vehicle Trajectory Prediction (VTP) …
Intelligent Transportation Systems (ITS). As a part of ITS Vehicle Trajectory Prediction (VTP) …
Deep Learning for Traffic Scene Understanding: A Review
This review paper presents an in-depth analysis of deep learning (DL) models applied to
traffic scene understanding, a key aspect of modern intelligent transportation systems. It …
traffic scene understanding, a key aspect of modern intelligent transportation systems. It …
A pov-based highway vehicle trajectory dataset and prediction architecture
Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for
various traffic safety and management applications. Despite the abundance of trajectory …
various traffic safety and management applications. Despite the abundance of trajectory …
Incremental learning-based real-time trajectory prediction for autonomous driving via sparse gaussian process regression
In the context of spatial-temporal autonomous driving, the accurate and real-time trajectory
prediction of the surrounding vehicle (SV) is crucial. This paper aims to design an efficient …
prediction of the surrounding vehicle (SV) is crucial. This paper aims to design an efficient …
CiPN-TP: a channel-independent pretrained network via tokenized patching for trajectory prediction
Trajectory prediction is highly essential for accurate navigation. Existing deep learning-
based approaches always encounter serious performance degradation when facing shifted …
based approaches always encounter serious performance degradation when facing shifted …
Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles
Connected and automated vehicles (CAVs) can be beneficial for improving the operation of
highway bottlenecks such as weaving sections. This paper proposes a bi-level control …
highway bottlenecks such as weaving sections. This paper proposes a bi-level control …
Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner
A Aiersilan - arxiv preprint arxiv:2412.18086, 2024 - arxiv.org
Motion planning is a crucial component in autonomous driving. State-of-the-art motion
planners are trained on meticulously curated datasets, which are not only expensive to …
planners are trained on meticulously curated datasets, which are not only expensive to …
Trajectory Prediction for Autonomous Driving using Agent-Interaction Graph Embedding
Trajectory prediction module in an autonomous driving system is crucial for the decision-
making and safety of the autonomous agent car and its surroundings. This work presents a …
making and safety of the autonomous agent car and its surroundings. This work presents a …
ForceGNN: A Force-Based Hypergraph Neural Network for Multi-agent Pedestrian Trajectory Forecasting
J Zhou, J Jiao, N Li - International Conference on Pattern Recognition, 2024 - Springer
Multi-agent trajectory prediction is crucial for many real-world applications. This task faces
challenges in effectively capturing individual temporal patterns and complex interactions …
challenges in effectively capturing individual temporal patterns and complex interactions …