Deep Convolutional Neural Network Applied to Quality Assessment for Video Tracking

2018/10/26 by Roger Gomez Nieto, Nieto, Roger Gomez, Eugenio Tamura Morimitsu +1
Computer Science · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques

paper · pdf · doi:10.48550/arxiv.1810.11550

openalex publication_date 2018/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• In-Capture Mobile Video Distortions: A Study of Subjective Behavior and Objective Algorithms
• Evaluation of Object Trackers in Distorted Surveillance Videos

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