Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection

Rui Dai
Quan Kong
  • Function : Author
Lorenzo Garattoni
  • Function : Author
  • PersonId : 1120055
Gianpiero Francesca
  • Function : Author
  • PersonId : 1023188

Abstract

Video anomaly detection in surveillance systems with only video-level labels (i.e. weakly-supervised) is challenging. This is due to, (i) complex integration of human and scene based anomalies comprising of subtle and sharp spatio-temporal cues in real-world scenarios, (ii) non-optimal optimization between normal and anomaly instances under weak-supervision. In this paper, we propose a Human-Scene Network to learn discriminative representations by capturing both subtle and strong cues in a dissociative manner. In addition, a self-rectifying loss is also proposed that dynamically computes the pseudo temporal-annotations from video-level labels for optimizing the Human-Scene Network effectively. The proposed Human-Scene Network optimized with self-rectifying loss is validated on three publicly available datasets i.e. UCF-Crime, ShanghaiTech and IITB-Corridor, outperforming recently reported state-of-the-art approaches on five out of the six scenarios considered.
Fichier principal
Vignette du fichier
HSN.pdf (380.74 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03946181 , version 1 (19-01-2023)

Identifiers

  • HAL Id : hal-03946181 , version 1

Cite

Snehashis Majhi, Rui Dai, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, et al.. Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection. 2023. ⟨hal-03946181⟩
29 View
21 Download

Share

Gmail Facebook X LinkedIn More