Video Latent Code Interpolation for Anomalous Behavior Detection - Archive ouverte HAL Access content directly
Conference Papers Year :

Video Latent Code Interpolation for Anomalous Behavior Detection

(1, 2) , (1, 2) , (1, 3) , (1, 3)
1
2
3

Abstract

Detecting an anomalous human behavior can be a challenging task. In this paper, we present a novel objective function for autoencoders which include a temporal component. Our method is a fully end-to-end semi-supervised approach for video anomaly detection. The autoencoder is trained to reconstruct a sample from a partial input, by interpolating latent codes obtained from this partial input. We show this approach improves over using usual autoencoder objective functions for video anomaly detection and achieves results close to the state of the art on a broad range of datasets. Our code is publicly available on github.
Fichier principal
Vignette du fichier
IAE__SMC_IEEE_.pdf (864.01 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03058296 , version 1 (11-12-2020)

Identifiers

  • HAL Id : hal-03058296 , version 1

Cite

Valentin Durand de Gevigney, Pierre-François Marteau, Arnaud Delhay, Damien Lolive. Video Latent Code Interpolation for Anomalous Behavior Detection. IEEE SMC 2020 - International Conference on Systems, Man, and Cybernetics, Oct 2020, Toronto / Virtual, Canada. ⟨hal-03058296⟩
64 View
258 Download

Share

Gmail Facebook Twitter LinkedIn More