Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Regularity in Skeleton Trajectories for Anomaly Detection in
  Videos   Appearance features have been widely used in video anomaly detection even
though they contain complex entangled factors. We propose a new method to model
the normal patterns of human movements in surveillance video for anomaly
detection using dynamic skeleton features. We decompose the skeletal movements
into two sub-components: global body movement and local body posture. We model
the dynamics and interaction of the coupled features in our novel
Message-Passing Encoder-Decoder Recurrent Network. We observed that the
decoupled features collaboratively interact in our spatio-temporal model to
accurately identify human-related irregular events from surveillance video
sequences. Compared to traditional appearance-based models, our method achieves
superior outlier detection performance. Our model also offers "open-box"
examination and decision explanation made possible by the semantically
understandable features and a network architecture supporting interpretability.

5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
2
The categories of this paper are: math