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Abstract
Deep classification tracking aims at classifying the candidate samples into target or background by a classifier generally trained with a binary label. However, the binary label merely distinguishes samples of different classes, while inadvertently ignoring the distinction among the samples belonging to the same class, which weakens the classification and locating ability. To cope with this problem, this article proposes a soft labeling with quasi-Gaussian structure instead of the binary labeling, which distinguishes the samples belonging to different classes and the same class simultaneously. Like as the binary label, the signs of labels for target and background samples are set to be plus and minus respectively to distinguish samples of different classes. Further, to exploit the difference among samples in the same class, the label values of samples in the same class are designed as a monotonically decreasing quasi-Gaussian function about Intersection over Union. Therefore, the corresponding response function is a two-piecewise monotonically increasing quasi-Gaussian combination function about Intersection over Union. Due to such response function, deep classification tracking trained with this proposed soft labeling achieves better classification and location performance. To validate this, the proposed soft labeling is integrated into the pipeline of the deep classification tracker SiamFC. Experimental results on OTB-2015 and VOT benchmark show that our variant achieves significant improvement to the baseline tracker while maintaining real-time tracking speed and acquires comparable accuracy as recent state-of-the-art trackers.
Highlights
Deep classification tracking treats object tracking as an object and background twocategory problem based on deep features, which classifies the samples into target or background through a classifier usually trained with the binary labeling
We firstly describe the problems of the binary labeling and propose a soft labeling with quasi-Gaussian structure for deep classification tracking
To overcome the drawbacks of the binary labeling, we propose a soft labeling with quasi-Gaussian structure instead of the binary labeling to enhance the classification and locating ability of deep classification tracking
Summary
One of the most important tasks in many robot applications, has been widely used in many fields such as intelligent manufacturing, human–computer interaction, video surveillance, and robotics.[1,2,3] It is an indispensable part of robots[4,5] serving as the “eye” for robots to communicate with the world as the Figure 1 shown. Such binary label has the ability to distinguish the samples of different classes but inadvertently overlooks the difference among samples in the same class This drawback makes the response map of deep classification tracking difficult to accurately reflect the target location. This article uses the IoU values as the design criteria and proposes a novel soft labeling with quasi-Gaussian structure instead of the binary labeling to distinguish samples belonging to different classes and the same class simultaneously. The third section describes the proposed soft labeling with quasi-Gaussian structure and applies it to the deep classification tracker SiamFC. A novel soft labeling with quasi-Gaussian structure is proposed to replace the binary labeling to enhance the classification and locating ability of deep classification tracking. Extensive experiments on OTB-2015 and VOT benchmark against many state-of-the-art trackers are performed, and tracking results demonstrate the superiority and efficiency of our proposed tracking algorithm
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