2,015 research outputs found
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
Deep learning models have been widely used for anomaly detection in
surveillance videos. Typical models are equipped with the capability to
reconstruct normal videos and evaluate the reconstruction errors on anomalous
videos to indicate the extent of abnormalities. However, existing approaches
suffer from two disadvantages. Firstly, they can only encode the movements of
each identity independently, without considering the interactions among
identities which may also indicate anomalies. Secondly, they leverage
inflexible models whose structures are fixed under different scenes, this
configuration disables the understanding of scenes. In this paper, we propose a
Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to
address these problems, the HSTGCNN is composed of multiple branches that
correspond to different levels of graph representations. High-level graph
representations encode the trajectories of people and the interactions among
multiple identities while low-level graph representations encode the local body
postures of each person. Furthermore, we propose to weightedly combine multiple
branches that are better at different scenes. An improvement over single-level
graph representations is achieved in this way. An understanding of scenes is
achieved and serves anomaly detection. High-level graph representations are
assigned higher weights to encode moving speed and directions of people in
low-resolution videos while low-level graph representations are assigned higher
weights to encode human skeletons in high-resolution videos. Experimental
results show that the proposed HSTGCNN significantly outperforms current
state-of-the-art models on four benchmark datasets (UCSD Pedestrian,
ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable
parameters.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technology (T-CSVT
Normalizing Flows for Human Pose Anomaly Detection
Video anomaly detection is an ill-posed problem because it relies on many
parameters such as appearance, pose, camera angle, background, and more. We
distill the problem to anomaly detection of human pose, thus reducing the risk
of nuisance parameters such as appearance affecting the result. Focusing on
pose alone also has the side benefit of reducing bias against distinct minority
groups. Our model works directly on human pose graph sequences and is
exceptionally lightweight ( parameters), capable of running on any
machine able to run the pose estimation with negligible additional resources.
We leverage the highly compact pose representation in a normalizing flows
framework, which we extend to tackle the unique characteristics of
spatio-temporal pose data and show its advantages in this use case. Our
algorithm uses normalizing flows to learn a bijective mapping between the pose
data distribution and a Gaussian distribution, using spatio-temporal graph
convolution blocks. The algorithm is quite general and can handle training data
of only normal examples, as well as a supervised dataset that consists of
labeled normal and abnormal examples. We report state-of-the-art results on two
anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the
recent supervised UBnormal dataset
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
Semi-supervised video anomaly detection (VAD) is a critical task in the
intelligent surveillance system. However, an essential type of anomaly in VAD
named scene-dependent anomaly has not received the attention of researchers.
Moreover, there is no research investigating anomaly anticipation, a more
significant task for preventing the occurrence of anomalous events. To this
end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes,
28 classes of abnormal events, and 16 hours of videos. At present, it is the
largest semi-supervised VAD dataset with the largest number of scenes and
classes of anomalies, the longest duration, and the only one considering the
scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for
video anomaly anticipation. We further propose a novel model capable of
detecting and anticipating anomalous events simultaneously. Compared with 7
outstanding VAD algorithms in recent years, our method can cope with
scene-dependent anomaly detection and anomaly anticipation both well, achieving
state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and
the newly proposed NWPU Campus datasets consistently. Our dataset and code is
available at: https://campusvad.github.io.Comment: CVPR 202
Human Computer Interactions in Next-Generation of Aircraft Smart Navigation Management Systems: Task Analysis and Architecture under an Agent-Oriented Methodological Approach
The limited efficiency of current air traffic systems will require a next-generation of Smart Air Traffic System (SATS) that relies on current technological advances. This challenge means a transition toward a new navigation and air-traffic procedures paradigm, where pilots and air traffic controllers perform and coordinate their activities according to new roles and technological supports. The design of new Human-Computer Interactions (HCI) for performing these activities is a key element of SATS. However efforts for developing such tools need to be inspired on a parallel characterization of hypothetical air traffic scenarios compatible with current ones. This paper is focused on airborne HCI into SATS where cockpit inputs came from aircraft navigation systems, surrounding traffic situation, controllers' indications, etc. So the HCI is intended to enhance situation awareness and decision-making through pilot cockpit. This work approach considers SATS as a system distributed on a large-scale with uncertainty in a dynamic environment. Therefore, a multi-agent systems based approach is well suited for modeling such an environment. We demonstrate that current methodologies for designing multi-agent systems are a useful tool to characterize HCI. We specifically illustrate how the selected methodological approach provides enough guidelines to obtain a cockpit HCI design that complies with future SATS specifications.This work was supported in part by Projects MINECO TEC2011-28626-C02-01/02, by program CENIT-ATLANTIDA (cofinanced by Indra and Boeing R&TE), and by ULPGC Precompetitive Research Project (ULPGC Own Program).Publicad
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