Online structured sparse learning with labeled information for robust object tracking

We formulate object tracking under the particle filter framework as a collaborative tracking problem. The priori information from training data is exploited effectively to online learn a discriminative and reconstructive dictionary, simultaneously without losing structural information. Specifically, the class label and the semantic structure information are incorporated into the dictionary learning process as the classification error term and ideal coding regularization term, respectively. Combined with the traditional reconstruction error, a unified dictionary learning framework for robust object tracking is constructed. By minimizing the unified objective function with different mixed norm constraints on sparse coefficients, two robust optimizing methods are developed to learn the high-quality dictionary and optimal classifier simultaneously. The best candidate is selected by minimizing the reconstructive error and classification error jointly. As the tracking continues, the proposed algorithms alternate between the robust sparse coding and the dictionary updating. The proposed trackers are empirically compared with 14 state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons demonstrate that the proposed algorithms perform well in terms of accuracy and robustness

Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness

Dual Graph Regularized Discriminative Multitask Tracker

Multi-task and low rank learning methods have attracted increasing attention for visual tracking. However, most trackers only focus on learning appearance subspace basis or the sparse low rankness of representation, thus do not make full use of the structure information among and inside target candidates (or samples). In this work, we propose a dual graph regularized discriminative low rank learning for multi-task tracker, which integrates the discriminative subspace and intrinsic geometric structures among tasks. By constructing double graphs regula- tions from two views of multi-task observation, the developed modal not only exploits the intrinsic relationship among tasks, and preserves the spatial layout structure among the local patches inside each candidate, but also learns the salient features of the target samples. This operation is benefit to having good target representation and improving the performance of the tracker. Moreover, our developed tracker is a collaborate multi- task tracking model, and learns the discriminative subspace with adaptive dimension and optimal classifier simultaneously. Then, a collaborate metric is developed to find the best candidate, which integrates both classification reliability and representation accu- racy. Encouraging experimental results on a large set of public video sequences justify that our tracker performs favourably against many other state-of-the-art trackers.</p

Dual Aligned Siamese Dense Regression Tracker

Anchor or anchor-free based Siamese trackers have achieved the astonishing advancement. However, their parallel regression and classification branches lack the tracked target information link and interaction, and the corresponding independent optimization maybe lead to task-misalignment, such as the reliable classification prediction with imprecisely localization and vice versa. To address this problem, we develop a general Siamese dense regression tracker (SDRT) with both task and feature alignments. It consists of two cooperative and mutual-guidance core branches: dense local regression with RepPoint representation, the global and local multi-classifier fusion with aligned features. They complement and boost each other to constrain the results with well-localized followed to also be well-classified. Specifically, a dense local regression with RepPoint representation, directly estimates and averages multiple dense local bounding box offsets for accurate localization. And then, the refined bounding boxes can be used to learn the global and local affine alignment features for reliable multi-classifier fusion. The classified scores in turn guide the assigned positive bounding boxes for the regression task. The mutual guidance operations can bridge the connection between classification and regression substantially, since the assigned labels of one task depend on the prediction quality of the other task. The proposed tracking module is general, and it can boost both the anchor or anchor-free based Siamese trackers to some extent. The extensive tracking comparisons on six tracking benchmarks verify its favorable and competitive performance over states-of-the-arts tracking modules.</p

Layered Multitask Tracker via Spatial-Temporal Laplacian Graph

Most multitask trackers define the trace of each candidate as one task, and assume all tasks are equally related. Multitask learning is only evaluated on the current frame. In fact, these assumptions are limited, and ignore the multitask relationship in consecutive frames. In this letter, we propose a discriminative layered multitask tracker via spatial-temporal Laplacian graphs, which defines the layered tasks from a novel view, and naturally incorporates the global and local target information into reverse multitask tracking process. The spatial-temporal Laplacian graphs not only exploit the sequential consistent information of the target, but also make full use of the geometric structure corresponding to the tasks among the adjacent frames. Besides, l(0) norm constraint and labeling information are used to improve the tracking robustness. Encouraging experimental results on challenging sequences justify that the proposed method performs well both in accuracy and robustness against some related trackers

Visual tracking system of rotorcraft UAV

设计实现了一种基于PC104计算平台的旋翼无人机自动目标跟踪系统。该系统由机载视觉子系统、地面站子系统、无线通信子系统3个部分组成,构建了完整的空地、人机交互环路。采用基于背景权重的Mean Shift目标跟踪算法,能够有效减小复杂环境背景对目标跟踪的影响,可靠性高且算法复杂度低。在室内外环境下进行的实验测试结果表明:系统在目标跟踪过程中即使遇到相似目标干扰或大面积遮挡,仍能够准确地自动跟踪目标,利用目标在图像中的位置主动引导数字云台与其保持相同运动方向,使目标尽可能处于相机中心视场范围内,验证了系统的可靠性和实时性

A unified online dictionary learning framwork with label information for robust object tracking

In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the unified objective function. By minimizing the unified objective function we learn the high quality dictionary and optimal linear multi-classifier jointly. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy and robustness

ACTIVE DRIFT CORRECTION TEMPLATE TRACKING ALGORITHM

This paper presents a novel active drift correction template tracking algorithm. Compared to Matthews&rsquo; algorithm in [8], the proposed algorithm achieves synchronously object tracking and drift correction, and save half running time. For the template drift problem during long sequential object tracking, we introduce the active drift correction term into inverse compositional affine image alignment algorithm. This operation can avoid the template drift before it occurs, or reduce the drift after it happens. The total energy function consists of two terms: the tracking term and the active drift correction term. By minimizing the total energy function with the steepest descent algorithm, the proposed algorithm can decrease the accumulative tracking error, and prevent the drift during the tracking process effectively. Various object tracking experiments show that our method has super performance than the passive drift correction algorithm in [8]

Reliable Multi-Kernel Subtask Graph Correlation Tracker

Many astonishing correlation filter trackers pay limited concentration on the tracking reliability and locating accuracy. To solve the issues, we propose a reliable and accurate cross correlation particle filter tracker via graph regularized multi-kernel multi-subtask learning. Specifically, multiple non-linear kernels are assigned to multi-channel features with reliable feature selection. Each kernel space corresponds to one type of reliable and discriminative features. Then, we define the trace of each target subregion with one feature as a single view, and their multi-view cooperations and interdependencies are exploited to jointly learn multi-kernel subtask cross correlation particle filters, and make them complement and boost each other. The learned filters consist of two complementary parts: weighted combination of base kernels and reliable integration of base filters. The former is associated to feature reliability with importance map, and the weighted information reflects different tracking contribution to accurate location. The second part is to find the reliable target subtasks via the response map, to exclude the distractive subtasks or backgrounds. Besides, the proposed tracker constructs the Laplacian graph regularization via cross similarity of different subtasks, which not only exploits the intrinsic structure among subtasks, and preserves their spatial layout structure, but also maintains the temporal-spatial consistency of subtasks. Comprehensive experiments on five datasets demonstrate its remarkable and competitive performance against state-of-the-art methods.</p

A Hybrid Tracking Method Based on Active Contour and Mean Shift Algorithm

Active contour is a very accurate in target tracking and robust to variety of illumination, translation, rotation and large scale. Unfortunately, the problem that active contour method can not track target in real time because of huge computation is still not well resolved. On the contrast, mean shift is a very fast algorithm in target tracking but sensitive to the change of illumination, and can&#39;t get the description of target&#39;s shape. In our work, we use mean shift method to determine the translation motion of target and build a initial rough contour for active contour method. So the computation of active contour method in early curves evolution stage is greatly reduced and the target can be tracked accurately by minimizing the energy function in few number of interactive computation. Experimental results validate our method.</p