Struck: Structured output tracking with kernels

TLDR

Adaptive tracking‑by‑detection methods treat tracking as a classification task, yet converting estimated positions into training examples and aligning classifier and tracker objectives remain unclear. This work introduces a framework for adaptive visual object tracking that uses structured output prediction. The framework employs a kernelized structured output SVM learned online, with a budgeting mechanism to keep the number of support vectors bounded for real‑time operation. Experiments show the algorithm surpasses state‑of‑the‑art trackers on benchmark videos and benefits from adding features and kernels.

Abstract

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (accurate estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we are able to avoid the need for an intermediate classification step. Our method uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow for real-time application, we introduce a budgeting mechanism which prevents the unbounded growth in the number of support vectors which would otherwise occur during tracking. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased performance.