Deep Convolutional Neural Network Applied to Quality Assessment for Video Tracking

TL;DR

This paper presents a deep convolutional neural network architecture designed to automatically detect exposure distortions in surveillance videos, aiming to improve video analysis accuracy and enable distortion-aware processing.

Contribution

The work introduces a novel deep learning architecture specifically for recognizing exposure distortions in videos, addressing a gap in automatic quality assessment.

Findings

Effective detection of exposure distortions demonstrated

Potential for enhancing video analysis pipelines

Foundation for distortion-aware video processing

Abstract

Surveillance videos often suffer from blur and exposure distortions that occur during acquisition and storage, which can adversely influence following automatic image analysis results on video-analytic tasks. The purpose of this paper is to deploy an algorithm that can automatically assess the presence of exposure distortion in videos. In this work we to design and build one architecture for deep learning applied to recognition of distortions in a video. The goal is to know if the video present exposure distortions. Such an algorithm could be used to enhance or restoration image or to create an object tracker distortion-aware.