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This paper proposes a robust object tracking algorithm dealing with the occlusion problem and illumination change. In particular, we judge whether each frame image is occluded by the confidence map, and two object spatial-temporal context models are established, one is updated every frame in the process of tracking, and the other is set up before the frame that the object is occluded. This method...
Patch strategy is widely adopted in visual tracking to address partial occlusions. However, most patch-based tracking methods either assume all patches sharing the same importance or exploit simple prior for computing the importance of each patch, which may depress the tracking performance when the target object is non-rigid or the background information is included in the initial bounding box. To...
The deep learning based trackers can always achieve high tracking precision and strong adaptability in different scenarios. However, due to the fact that the number of the parameter is large and the fine-tuning is challenging, the time complexity is high. In order to improve the efficiency, we proposed a tracker based on fast deep learning through constructing a new network with less redundancy. Based...
In order to cope with the complex variation of target appearance during visual tracking, a robust tracking algorithm based on multi-scale kernelized least squares (KLS) is proposed. First, by showing that the dense sampling set of translated patches is circulant, using the well-established theory of circulant matrices, kernelized least squares is efficient computed with fast Fourier transform (FFT)...
One of the most challenging problems faced by tracking algorithms is the issue of template drift. For robust object tracking, template should be adaptive enough to incorporate maximum changes of target appearance, and at same time it should be restrictive enough to reject any background information entering into its model so that drifting of template may be avoided. The existing template updating...
Correlation filter based tracking method has been widely used for its high efficiency and robustness. However, reducing model drifting while achieving both high robustness and fast scale estimation is still an open problem. In this paper, we represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary...
In this paper, we propose a keyframe-based online object learning and detection method. To manage appearance changes of target objects, the proposed method incrementally updates an object database using detection results. One of the major problems in updating the appearance model is that the object model can gradually be degraded by accumulated errors and biased to specific views. To solve this problem,...
We present a tracking system based on ultra-wideband (UWB) radio tranceivers mounted on a robot and a target. In comparison to typical UWB localization systems with fixed UWB tranceivers in the environment we only require instrumentation of the target with a single UWB tranceiver. Our system works in GPS-denied environments and does not suffer from long-term drift and limited fields of view. This...
Autonomous robots enjoy a wide popularity nowadays and have been applied in many applications, such as home security, entertainment, delivery, navigation and guidance. It is vital for robots to track objects accurately in real time in these applications, so it is necessary to focus on tracking algorithms to improve the robustness, speed and accuracy. In this paper, we propose a real-time robust object...
In this paper, we propose a persistent, robust and autonomous object tracking system for unmanned aerial vehicles (UAVs) called Persistent Aerial Tracking (PAT). A computer vision and control strategy is applied to a diverse set of moving objects (e.g. humans, animals, cars, boats, etc.) integrating multiple UAVs with a stabilized RGB camera. A novel strategy is employed to successfully track objects...
In this paper, we propose a compressive sensing based framework for robust visual tracking. As a key part of the tracking framework, a new multi-task sparse learning method is designed to estimate the observation likelihood in order to determine the best target. Compared with the traditional multi-task sparse learning method, our method uses compressed appearance features to achieve multi-task sparse...
In this paper, we propose an image target tracking algorithm for an embedded platform. Our proposed can process 1280 × 720 resolution video sequences and provide accurate image tracking in real time. In the tracking algorithm, an adaptive local edge detection method is employed to extract the feature pixels of a tracked object. To reduce tracking errors, a region-based local binary pattern feature...
Robust and accurate visual tracking is needed for many computer vision applications from video summarization to visual surveillance. Visual tracking remains to be a challenging task because of factors such as changing object appearance, illumination variations and shadows, partial and full occlusions, camera motion, distractors, and scale changes. Recently our group proposed a Likelihood of Features...
Accurate and efficient object tracking is an important aspect of various security and surveillance applications. In object tracking solutions which utilize intensity-based histogram feature methods for use on wide area motion imagery (WAMI), there currently exists tracking challenges due to object structural information distortions and pavement/background variations. The inclusion of structural target...
In order to improve the docking success rate in Automated Aerial Refueling (AAR), it is important to identify the receiver aircraft's receptacle for boom receptacle refueling (BRR). Meanshift tracking algorithm only considers the H component color statistics of the target area, lacking spatial information, could easily lead to inaccurate tracking. Besides, Meanshift tracking algorithm could easily...
The goal of visual tracking is to estimate the location of the visual target in a sequence of images. If the video has long-term sequence of images, the tracking task without prior information is severely difficult due to the occlusion or the disappearance. Recently, tracking-by-detection methods have been proposed to solve the long-term tracking problem. However, most of them also suffer from drifting,...
Visual tracking is one of the hot research topics in computer vision in recent years. It has been widely used in many vision applications, such as traffic surveillance, anti-terrorism. However, there are still challenges for visual tracking, like illumination change, object occlusion, appearance deformation, etc. This paper proposes a robust point detection algorithm based on wavelet transform for...
Object tracking is one of the important tasks for mobile robot, and developing a robust and real-time visual tracking algorithm which can adaptively capture the varying appearance of target under challenging conditions for mobile robot is still an open problem. The main challenges of visual tracking for mobile robot come from variation of target's appearance and disturbance of environment. To cope...
In this paper we present and evaluate an algorithm for tracking vessels in oceanographic airborne image sequences on the visible spectrum. Such sequences are challenging due to sun reflections, wakes, wave crests and fast motions, which significantly degrade the performance of general purpose tracking algorithms. The proposed method is based on state-of-the-art correlation filter tracking complemented...
To improve the tracking robustness in fuzziness when the underwater targets are overlapped in forward-looking sonar images, a contour tracking method that use Local Binary Fitting (LBF) to constrain the evolution of particle filter is discussed for underwater targets tracking in this paper. A space prior is generated from underwater target contour information which is used to restrict the boundary...
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