[HTML][HTML] Evaluation of socially-aware robot navigation
As mobile robots are increasingly introduced into our daily lives, it grows ever more
imperative that these robots navigate with and among people in a safe and socially …
imperative that these robots navigate with and among people in a safe and socially …
[HTML][HTML] Review on deep learning approaches for anomaly event detection in video surveillance
In the last few years, due to the continuous advancement of technology, human behavior
detection and recognition have become important scientific research in the field of computer …
detection and recognition have become important scientific research in the field of computer …
Self-supervised predictive convolutional attentive block for anomaly detection
Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …
models can only learn from normal training samples, while being evaluated on both normal …
Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning
Demystifying the interactions among multiple agents from their past trajectories is
fundamental to precise and interpretable trajectory prediction. However, previous works only …
fundamental to precise and interpretable trajectory prediction. However, previous works only …
Ubnormal: New benchmark for supervised open-set video anomaly detection
A Acsintoae, A Florescu… - Proceedings of the …, 2022 - openaccess.thecvf.com
Detecting abnormal events in video is commonly framed as a one-class classification task,
where training videos contain only normal events, while test videos encompass both normal …
where training videos contain only normal events, while test videos encompass both normal …
Unknown-aware object detection: Learning what you don't know from videos in the wild
Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical
yet underexplored. One of the key challenges is that models lack supervision signals from …
yet underexplored. One of the key challenges is that models lack supervision signals from …
Scept: Scene-consistent, policy-based trajectory predictions for planning
Trajectory prediction is a critical functionality of autonomous systems that share
environments with uncontrolled agents, one prominent example being self-driving vehicles …
environments with uncontrolled agents, one prominent example being self-driving vehicles …
Intention-aware vehicle trajectory prediction based on spatial-temporal dynamic attention network for internet of vehicles
X Chen, H Zhang, F Zhao, Y Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV).
With the help of deep learning and big data, it is possible to understand the between-vehicle …
With the help of deep learning and big data, it is possible to understand the between-vehicle …
Remember intentions: Retrospective-memory-based trajectory prediction
To realize trajectory prediction, most previous methods adopt the parameter-based
approach, which encodes all the seen past-future instance pairs into model parameters …
approach, which encodes all the seen past-future instance pairs into model parameters …
Self-supervised attentive generative adversarial networks for video anomaly detection
Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos.
The deep generative model (DGM)-based method learns the regular patterns on normal …
The deep generative model (DGM)-based method learns the regular patterns on normal …