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Robot human interactions have significant applications in dealing with day-to-day robot service. This paper presents a novel approach for human detection, tracking and following. A flexible and robust detection method using a 2D laser scanner is proposed firstly. The detection method can be adapted to various leg characteristics. The detection result is then fed to a Kalman Filter based tracking algorithm...
Robust visual object tracking against occlusions and deformations is still very challenging task. To tackle these issues, existing Convolutional Neural Networks (CNNs) based trackers either fail to handle them or can just run in low speed. In this paper, we present a realtime tracker which is robust to occlusions and deformations based on a Region-based, Multi-Scale Fully Convolutional Siamese Network...
The paper presents an approach for tracking a variable number of objects by using a multi-layer particle filter combined with an extended Expectation Maximization (EM) clustering. The approach works on basis of binary foreground images coming from previous background subtraction. The multi-layer particle filter is an improvement of a conventional particle filter approach. It uses an introduced adaptive...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
In this paper, the non-fragile finite-time tracking control problem is addressed for a class of uncertain linear systems with controller multiplicative coefficient variations. An adaptive control strategy is constructed to ensure that the system tracks a time-varying target orbit. The relationship of the bound of tracking errors and the size of uncertainties and controller multiplicative coefficient...
Visual tracking is a challenging task due to a number of factors, such as occlusions, deformations, illumination variations and abrupt motion changes present in a video sequence. Generally, trackers are robust to some of these factors, but do not achieve satisfactory results when dealing with multiple factors at the same time. More robust results when multiple factors are present can be obtained by...
This paper presents a new control law for three-dimensional path following of underactuated autonomous underwater vehicles (AUV). The kinematic controller based on the Serret-Frenet frame is firstly designed, which is convenient to describe the track errors. It also overcomes the stringent initial condition constraints by introducing virtual target. Moreover, the dynamic controller based on Lyapunov...
The technique of Projection Mapping, which is useful for merging real-world geometry with an augmented appearance, is a promising core technology for augmented reality (AR). In recent years, dynamically changing environments, mainly a consequence of the growing demand for interactive user experiences, have contributed to a new style of AR applications. However, performance levels of current systems...
Probabilistic tracking algorithms typically using linear structure to update the learning model. Such linear structure is not appropriate for long-term robust tracking as the occlusion and other challenging factors may interfere the processing of incoming frames. Recently a spatio-temporal context (STC) algorithm based on Bayesian framework has using the context information between the target and...
In this paper, we propose a robust visual tracking method which exploits the relationships of targets in adjacent frames using patchwise joint sparse representation. Two sets of overlapping patches with different sizes are extracted from target candidates to construct two dictionaries with consideration of joint sparse representation. By applying this representation into structural sparse appearance...
To overcome visual object tracking challenges, various feature-based object trackers use feature combination. Each feature component is developed to overcome certain tracking challenges, but the interaction between the components may cause tracking errors. We propose a tracking solution based on human vision principles to reduce combination errors by adaptively fusing each feature using its previous...
Part-based trackers are effective in exploiting local details of the target object for robust tracking. In contrast to most existing part-based methods that divide all kinds of target objects into a number of fixed rectangular patches, in this paper, we propose a novel framework in which a set of deformable patches dynamically collaborate on tracking of non-rigid objects. In particular, we proposed...
Visual object tracking is one of the basic units in the construction of smart cities, which focuses on establishing a dynamic appearance model to represent and recognize the target in complex scenarios. In this paper, we consider visual object tracking as multiple local patches matching problem and design an online tracker based on correlation filter and binary descriptors. We integrate binary descriptors...
Significant development in path planning algorithms for unmanned aerial vehicles (UAVs) has been performed using numerous different methods. One such method, Partially Observable Markov Decision Processes (POMDP), has been used effectively for tracking fixed and moving targets. One limitation of those efforts has been the assumption that the UAVs could always see the targets, with a few unique exceptions,...
During target tracking, in order to obtain a higher tracking accuracy, the region we would like to track should have a good feature expression. Furthermore, we need to extract multilevel and complex features to deal with problems which are usually encountered during UAV tracking, such as the target deformation, scale change and occlusion. However, such features make tracker more complex which would...
Persistent detection and tracking of moving vehicles in airborne imagery provide indispensable information for many traffic surveillance applications including traffic monitoring and management, navigation systems, activity recognition and event detection. This paper presents a collaborative Spatial Pyramid Context-aware detection and Tracking system (SPCT) for moving vehicles in dense urban aerial...
In object tracking, a novel tracking framework which is called “Tracking-Leaning-Detection” was proposed by Zdenka Kalal. This framework decomposes the object tracking task into tracking, learning and detection. In every frame that follows, the tracker and the detector work simultaneously to obtain the location of the object independently, and the learning acts as an information exchanging center...
Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional...
We propose Quadruplet Convolutional Neural Networks (Quad-CNN) for multi-object tracking, which learn to associate object detections across frames using quadruplet losses. The proposed networks consider target appearances together with their temporal adjacencies for data association. Unlike conventional ranking losses, the quadruplet loss enforces an additional constraint that makes temporally adjacent...
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