#251:
N. Al-Shakarji,
F. Bunyak,
G. Seetharaman, and
K. Palaniappan
IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS),
pgs. 1-7,
2017
Abstract,
Bibtex,
PlainText,
PDF,
URL,
DOI,
Google Scholar
Multi-object tracking is an important computer vision task with wide variety of real-life applications from surveillance and monitoring to biomedical video analysis. Multi-object tracking is a challenging problem due to complications such as partial or full occlusions, factors affecting object appearance, object interaction dynamics, etc. and computational cost. In this paper, we propose a detection-based multi-object tracking system that uses a two-step data association scheme to ensure time efficiency while preserving tracking accuracy; a robust but discriminative object appearance model that compares object color attributes using a novel color correlation cost matrix; and a framework that handles occlusions through prediction. Our experiments on UA-DETRAC multi-object tracking benchmark dataset consisting of challenging real-world traffic videos show promising results against state-of-the-art trackers.
@inproceedings{Noor2017:AVSS_ColorCorMTX,
author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
title = "Robust multi-object tracking with semantic color correlation",
year = 2017,
journal = "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
pages = "1-7",
keywords = "image color analysis, tracking, color, correlation, lighting, histograms.",
doi = "10.1109/AVSS.2017.8078507",
url = "https://ieeexplore.ieee.org/abstract/document/8078507/"
}
N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Robust multi-object tracking with semantic color correlation. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1-7, 2017.
