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AbstractłIn order to enhance the robustness of kernel correlation filters(KCF) in complex background environment, this paper proposes a mean shift method with adaptive local object tracking algorithm. KCF algorithm has speed advantage by using the single template, we introduce the confidence map in the process of the tracking to determine the result of the current frame. If the result of confidence...
Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on ImageNet as a feature extractor for visual tracking. However, most of their works...
Convolutional neural networks are widely used in object recognition and detection. In recent years, some researchers attempt to apply deep neural networks to visual object tracking. However, deep networks are extremely time-consuming and object tracking is not a classification problem essentially. In this paper, we present an online tracking framework which combines shallow convolutional neural networks...
Visual object tracking is a fundamental task in many high-level computer vision applications. Most existing algorithms have to build complex models with expensive computation to achieve accurate object tracking, which brings significant difficulty in real-time tracking. In order to address this problem, motivated by recent success of high-speed correlation filter (CF) models, a novel real-time object...
Object tracking is an important task within the field of computer vision. Object tracking methodology is divided into three categories: point-, kernel-; and silhouette-based tracking. Recently, a correlation-based kernel tracking has been used in object tracking with high accuracy and performance. However, the correlation-based approach heavily relies on luminance information, thus it has many problems...
In recent years, correlation filter based trackers outperform better than other trackers. Nevertheless, they only employ one feature and a single kernel, so they are usually not robust in complex scenes. In this paper, we derive a multi-feature and multi-kernel correlation filter based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features and kernels...
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
Correlation filter-based trackers achieve very good performance in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the image sequence. To solve this problem, we propose a novel and robust scale adaptive tracker combined with color attributes in correlation filter framework, which extracts not only gray but also color information as the feature...
Circulant Structure Kernel (CSK) has recently been introduced as a simple and extremely efficient tracking method. In this paper, we propose an extension of CSK that explicitly addresses partial occlusion problems which the original CSK suffers from. Our extension is based on a part-based scheme, which improves the robustness and localisation accuracy. Furthermore, we improve the robustness of CSK...
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