Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector

TL;DR

This paper introduces a computationally efficient method for detecting spatiotemporal visual saliency in videos using phase spectrum analysis, enhancing applications like abnormality detection and interest point identification.

Contribution

It presents a novel phase spectrum-based approach for spatiotemporal saliency detection that is simple to implement and effective, outperforming existing methods in key vision tasks.

Findings

Effective in abnormality detection

Improves interest point detection accuracy

Outperforms state-of-the-art methods

Abstract

Visual saliency, which predicts regions in the field of view that draw the most visual attention, has attracted a lot of interest from researchers. It has already been used in several vision tasks, e.g., image classification, object detection, foreground segmentation. Recently, the spectrum analysis based visual saliency approach has attracted a lot of interest due to its simplicity and good performance, where the phase information of the image is used to construct the saliency map. In this paper, we propose a new approach for detecting spatiotemporal visual saliency based on the phase spectrum of the videos, which is easy to implement and computationally efficient. With the proposed algorithm, we also study how the spatiotemporal saliency can be used in two important vision task, abnormality detection and spatiotemporal interest point detection. The proposed algorithm is evaluated on several commonly used datasets with comparison to the state-of-art methods from the literature. The experiments demonstrate the effectiveness of the proposed approach to spatiotemporal visual saliency detection and its application to the above vision tasks