PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Forward-Backward Error: Automatic Detection of Tracking Failures
Zdenek Kalal, Krystian Mikolajczyk and Jiri Matas
ICPR 2010.

Abstract

This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories in video sequences. We demonstrate that the approach is complementary to commonly used normalized cross-correlation (NCC). Based on the error, we propose a novel object tracker called Median Flow. State-of-the-art performance is achieved on challenging benchmark video sequences which include non-rigid objects.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6952
Deposited By:Zdenek Kálal
Deposited On:17 June 2010