Abstract
Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target’s appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance.
Highlights
Research interest in visual object tracking comes from the fact that it is widely used in smart and autonomous systems, e.g., anomaly detection, smart video compression, and driver intelligent assistance systems
To get a more general adaptive tracker, researchers have proposed many tracking approaches using various visual representations. These approaches, which are mainly focused on the research of the object appearance model, can be divided into two categories: generative models and discriminative models
We evaluate the proposed tracking algorithm and other six state-of-the-art tracking algorithms on nine challenging sequences including occlusion from a similar object to demonstrate the effectiveness of the proposed approach for alleviating similar object interference
Summary
Research interest in visual object tracking comes from the fact that it is widely used in smart and autonomous systems, e.g., anomaly detection, smart video compression, and driver intelligent assistance systems. To get a more general adaptive tracker, researchers have proposed many tracking approaches using various visual representations These approaches, which are mainly focused on the research of the object appearance model, can be divided into two categories: generative models and discriminative models. Generative models [1,2,3,4,5] search the closest description in model space as the target observation to estimate target state These models adopt an appearance model to describe the target appearance state without considering the background information of the target effectively. They have low discrimination when scene is complex. Discriminative models [6,7,8]
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