Content-aware vision-based vehicle tracking for frame-skipped videos
Date of Publication
2021
Document Type
Master's Thesis
Degree Name
Master of Science in Computer Science
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Advisor
Joel P. Ilao
Defense Panel Chair
Marnel S. Peradilla
Defense Panel Member
Neil Patrick A. Del Gallego
Abstract/Summary
Vision-based object tracking aims to approximate the trajectory of an object as it is observed to move in a video. Mainstream algorithms for this task assume that the object’s motion is smooth and require high frame rates. However, camera networks that stream video exhibit frame skipping, which is a visual degradation that causes object motion to be disjointed. This work addressed the frame skipping problem on the object tracking algorithm level by leveraging content information and a dynamic selection of motion and appearance features. A motion-based tracking algorithm is used if the video possesses a high frame rate and an appearance-based tracking algorithm is used if the frame rate is low. The performance of the tracking algorithm is assessed using ClearMOT metrics. The tracking algorithm developed in this work outperformed the classical tracking algorithm.
Abstract Format
html
Language
English
Format
Electronic
Physical Description
199 leaves
Keywords
Computer vision; Streaming video
Recommended Citation
Cempron, J. C. (2021). Content-aware vision-based vehicle tracking for frame-skipped videos. Retrieved from https://animorepository.dlsu.edu.ph/etdm_comsci/3
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Embargo Period
5-31-2021