@inproceedings{fde7d2af01454878b266584be446b63e,
title = "Multi-class classification and clustering based multi-object tracking",
abstract = "In this paper, we study the challenging problem of tracking multiple moving objects by a single camera. In our approach, a novel and efficient way to obtain an appearance and temporal based tracklet affinity model is proposed. We also propose to formulate the tracking problem as a classification task, where we classify tracklets into multi-classes, jointly across space and time. In our framework, we formulate our objective as a decision function with a Bayesian classifier constraint. We learn a model which seeks to maximize the decision function whilst minimizing our constraint. We estimate the probability density function of each class using a Gaussian probability density function, which reduces our misclassification loss. Our tracklet generation method minimizes the effect of missed detections and false positives. The proposed method emphasizes the effectiveness of multi-class SVMs (Support Vector Machines) in Multi-object tracking (MOT). Experimental results on three widely used Multi-Object Tracking datasets show that our method outperforms several state-of-the-art approaches in multi-object tracking.",
keywords = "Classifier, Multi-class, Support vector machine, Tracklet",
author = "Sowah, {Nii Longdon} and Qingbo Wu and Fanman Meng",
year = "2017",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "480--484",
editor = "Hukins, {David WL} and Korsunsky, {A. M.} and Len Gelman and Ao, {S. I.} and Andrew Hunter",
booktitle = "Proceedings of the World Congress on Engineering 2017, WCE 2017",
note = "2017 World Congress on Engineering, WCE 2017 ; Conference date: 05-07-2017 Through 07-07-2017",
}