Spatio-temporal predictive tasks for abnormal event detection in videos
- URL: http://arxiv.org/abs/2210.15741v2
- Date: Sun, 23 Apr 2023 16:35:35 GMT
- Title: Spatio-temporal predictive tasks for abnormal event detection in videos
- Authors: Yassine Naji, Aleksandr Setkov, Ang\'elique Loesch, Mich\`ele
Gouiff\`es, Romaric Audigier
- Abstract summary: We propose new constrained pretext tasks to learn object level normality patterns.
Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics.
Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
- Score: 60.02503434201552
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Abnormal event detection in videos is a challenging problem, partly due to
the multiplicity of abnormal patterns and the lack of their corresponding
annotations. In this paper, we propose new constrained pretext tasks to learn
object level normality patterns. Our approach consists in learning a mapping
between down-scaled visual queries and their corresponding normal appearance
and motion characteristics at the original resolution. The proposed tasks are
more challenging than reconstruction and future frame prediction tasks which
are widely used in the literature, since our model learns to jointly predict
spatial and temporal features rather than reconstructing them. We believe that
more constrained pretext tasks induce a better learning of normality patterns.
Experiments on several benchmark datasets demonstrate the effectiveness of our
approach to localize and track anomalies as it outperforms or reaches the
current state-of-the-art on spatio-temporal evaluation metrics.
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