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Journal Articles IEEE Transactions on Image Processing Year : 2017

Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects

Abstract

In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained “in one shot” at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking.
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Dates and versions

hal-01574513 , version 1 (14-08-2017)

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Stefan Duffner, Christophe Garcia. Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects. IEEE Transactions on Image Processing, 2017, 26 (5), pp.2368 - 2380. ⟨10.1109/TIP.2017.2676346⟩. ⟨hal-01574513⟩
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