Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137978
Title: Real time multiple object tracking with improved object interference handling
Authors: Leow, Rou Shan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2020
Publisher: Nanyang Technological University
Project: SCSE19-0463
Abstract: Having the ability to track multiple objects in video sequences by object detection can result in not only useful applications such as video surveillance but also problems which includes object re-identification, poor motion prediction and occlusion. While multi-object tracking (MOT) is a deeply explored area, it is not yet successfully solved using computer vision methods and artificial intelligence. Due to highly dynamic environment in MOT, objects often collide and occlude each other hence resulting in false initializing and ID switching. In order to reduce the occurrence of these issues, this paper presents a simple method which integrates existing tracker and various methods such as calculating the cosine differences in visual features and changing the relevant thresholds, to improve the accuracy. This tracking system is evaluated on MOT17 benchmark detection rubrics and the results achieved showed improvements as compared to having no such extensions.
URI: https://hdl.handle.net/10356/137978
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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