Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Topkaya, Ibrahim; Erdogan, Hakan; Porikli, Fatih
• Is Part Of: Signal, image and video processing. Volume 10:Issue 5(2016)
• Page Start: 795
• Page End: 802
• Publication Date: 2016-07
• Subjects: Distance dependent Chinese restaurant process -- Tracklet clustering -- Object tracking; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0817-x
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract To contrive an accurate and efficient strategy for object detection–object track assignment problem, we present a tracklet clustering approach using distance dependent Chinese restaurant processes (ddCRPs), which employ a two-level robust object tracker. The first level is an ordinary tracklet generator that obtains short yet reliable tracklets. In the second level, we cluster the tracklets over time based on color, spatial and temporal attributes, where the nonparametric process of clustering with ddCRPs allows us to maintain an unknown number of objects. Unlike the previously proposed Chinese restaurant processes and Dirichlet process mixture models, our ddCRPs method does not require prescribed complex cluster models to be initialized and updated, and thus, we can cluster complex tracklets by only computing similarities between them. Our comparative evaluations on tracking different object types demonstrate the generality of our approach.