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Being intensively studied, visual tracking has seen great recent advances in either speed (e.g., with correlation filters) or accuracy (e.g., with deep features). Real-time and high accuracy tracking algorithms, however, remain scarce. In this paper we study the problem from a new perspective and present a novel parallel tracking and verifying (PTAV) framework, by taking advantage of the ubiquity...
Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly explored in standard benchmarks due to weakly defined, biased and overlapping attribute annotations. In this paper we propose to go beyond pre-recorded benchmarks with post-hoc annotations...
A novel approach for robot tracking and identification based on barcodes is proposed in this paper. The proposed system tracks robots fitted with barcodes identifying them. The system performs distributed visual processing and collaborative barcode tracking, whereby the nodes exchange processed visual information with appropriate neighboring nodes, informing them about incoming targets. The proposed...
How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking. Recently, Siamese networks have shown great potentials of matching based trackers in achieving balanced accuracy and beyond realtime speed. However, they still have a big gap to classification...
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...
This paper investigates dynamic visual feedback position tracking of two kinds of two-wheeled vehicles with a camera or a target object for visual measurements. A problem formulation is first provided, where the dynamics of the camera vehicle is explicitly considered and the target vehicle velocity is expressed by a Fourier series expansion. Next, passivity-based tracking control structure is explained...
We propose an active visual tracker with collision avoidance for camera-equipped robots in dense multi-agent scenarios. The objective of each tracking agent (robot) is to maintain visual fixation on its moving target while updating its velocity to avoid other agents. However, when multiple robots are present or targets intensively intersect each other, robots may have no accessible collision-avoiding...
A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize the target object. Such method relies on collective competence and the diversity of the ensemble to approach the target/non-target classification task from different views. However, by updating all of the ensemble using a...
This paper presents an online multiple pedestrian detection and tracking method using unified multi-channel features. The proposed method efficiently utilizes the multi-channel features by sharing them in each module: pedestrian detection, visual tracking, and data association. The multi-channel features are originally generated from the pedestrian detection module, and they represent sufficiently...
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...
This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC...
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...
The tracking methods under the online multiple instance learning(MIL) framework always use single channel's information or transform the RGB image to gray image for color video tracking, which may cause the information loss of the color image. In addition, the MIL tracker only use the haar-like feature to construct the target appearance and ignore the different samples have different importance to...
In this paper, a landing control problem is investigated in which a quadrotor is required to autonomously land on a desired area of a ground moving target. In order to guarantee the reliability and continuity of the target recognition, the path planning of the quadrotor has to be considered under the constraints of its position, velocity and acceleration. Due to the affection of the velocity measurement...
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...
At present, the effective tracking of pedestrians is still a challenging task due to factors such as illumination change, pose variation, motion blur and occlusion. In this paper, we propose a simple and effective tracking algorithm which exploits the spatio-temporal context. Based on a existing Bayesian framework, we take full advantage of the relevance of the region of interest to its local context,...
Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much...
In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. The proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking...
Composed of multi-section precurved tubes, continuous concentric-tube robot(CTR) has the potential of reaching surgical target during minimally invasive surgeries. Since concentric tubes are made of super-elastic nickel-titanium alloy, they can present different shapes when they extend and rotate with respect to each other. Compared to traditional surgical robots, CTR is superior in small size and...
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