Title: MULTIPLE-KERNEL ADAPTIVE SEGMENTATION AND TRACKING (MAST) FOR ROBUST OBJECT TRACKING
Authors: Tang, Zheng
Hwang, Jenq-Neng
Lin, Yen-Shuo
Chuang, Jen-Hui
資訊工程學系
Department of Computer Science
Keywords: Adaptive Segmentation;Object Tracking;Multiple Kernels;Background Subtraction;Shadow Removal
Issue Date: 2016
Abstract: In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.
URI: http://hdl.handle.net/11536/136360
ISBN: 978-1-4799-9988-0
ISSN: 1520-6149
Journal: 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS
Begin Page: 1115
End Page: 1119
Appears in Collections:Conferences Paper