Object-centric video anomaly detection with covariance features [Kovaryans öznitelikleri ile nesne merkezli video anomali sezimi]

Bilecen, Ali Enver and Özkan, Hüseyin (2022) Object-centric video anomaly detection with covariance features [Kovaryans öznitelikleri ile nesne merkezli video anomali sezimi]. In: 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey

Full text not available from this repository. (Request a copy)

Abstract

In this paper, we propose four different methods for object-centric anomaly detection in surveillance videos based on autoregressive probability estimation. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. To decide whether an observation sequence (i.e. a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. By employing an object detection module to determine the important active regions in a scene with high detection rate, we propose new long-short term memory (LSTM), linear regression, and Gaussian mixture based methods to model the probability density of observation sequences. The most successful methods we propose achieves an average performance of 0.843 and 0.935 AUC scores respectively on two publicly available benchmark datasets.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: anomaly detection; covariance features; Gaussian mixture model; long short-term memory; object detection
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Hüseyin Özkan
Date Deposited: 25 Mar 2023 15:11
Last Modified: 25 Mar 2023 15:11
URI: https://research.sabanciuniv.edu/id/eprint/45111

Actions (login required)

View Item
View Item