Fragment-based Visual Tracking with Multiple Representations

Junqiu Wang,Yasushi Yagi

doi:10.3844/ajeassp.2016.187.194

Junqiu Wang, Yasushi Yagi

Open Access

https://doi.org/10.3844/ajeassp.2016.187.194

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Abstract

We present a fragment-based tracking algorithm that considers appearance information characterized by a non-parametric distribution and spatial information described by a parametric representation. We segment an input object into several fragments based on the appearance similarity and spatial distribution. Spatial distribution and appearance are important for distinguishing different fragments. We employee such information for separating an object from its background: Appearance information is described by nonparametric representation such as kernels; spatial information is characterized by Gaussians with spatial distribution of fragments. We integrate appearance and spatial information for target localization in images. The overall motion is estimated by the mean shift algorithm. This motion can deviate from the true position in the overall motion estimation because of the mean-shift drifting. We refine the estimated position based on the foreground probabilities. The proposed tracker gives better target localization results and better foreground probability images. Our experimental results demonstrate that the integration of appearance and spatial information by combining parametric and non-parametric representation is effective for tracking targets in difficult sequences.

Highlights

Visual tracking is still a hard problem after the intensive investigation over the years
Adaptive tracking (Collins et al, 2005; Han and Davis, 2004; Wang and Yagi, 2013) is effective for improving the tracking accuracy of a tracking algorithm by choosing good features that distinguish the object against its background
This problem can be partially solved by using an effective representation of targets and their background

Summary

Introduction

Visual tracking is still a hard problem after the intensive investigation over the years. Adaptive tracking (Collins et al, 2005; Han and Davis, 2004; Wang and Yagi, 2013) is effective for improving the tracking accuracy of a tracking algorithm by choosing good features that distinguish the object against its background. To make the target model adaptive to the appearance variations, the tracker has to classify the pixels in the region into foreground and background. The misclassification of the pixels can lead to the failure of the trackers for adaptive model updating This problem can be partially solved by using an effective representation of targets and their background. We make a feature pool by representing an object by color and shape texture cues. The proposed tracking algorithm describes appearance information by nonparametric representation (kernels). We refine the motion parameters by searching in the foreground probability images

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Conclusion