A registration-based tracking algorithm based on noise separation

Object tracking in real-world scenarios is often hampered by the simultaneous challenges of low light and partial object occlusion. While existing evaluation datasets have tackled these scenarios separately-focusing either on low-light settings or amodal perception-their co-occurrence has rarely been studied. To bridge this gap, we introduce LAOT (Low-Light Amodal Object Tracking), a benchmark tailored to assess how state-of-the-art tracking algorithms perform under the combined challenges of dim lighting and partial object occlusion. The dataset consists of 201 diverse video sequences with over 16K frames, each meticulously annotated with both modal bounding boxes (representing the visible part of the object) and amodal bounding boxes (estimating the complete object extent, including occluded regions). To characterize occlusion severity, object instances are categorized into three levels based on visible proportion: 0-20% (no occlusion), 20-80% (partial occlusion), and 80-100% (heavy occlusion). We benchmark 21 state-of-the-art tracking algorithms, including ARTrack [1], AVTrack [2], DropTrack [3], SeqTrack [4], ProContEXT [5], and HIPTrack [6], using Average Precision (AP) as the evaluation metric across occlusion levels. The results reveal a consistent decline in tracking accuracy as occlusion increases. For example, DropTrack achieves 0.8382 AP without occlusion but drops to 0.1730 AP with heavy occlusion; similarly, AVTrack falls from 0.7375 to 0.1276. These findings expose the limitations of current methods in coping with simultaneous challenges in low light and amodal perception. LAOT serves as a comprehensive and diagnostic benchmark to guide the development of robust perception-aware tracking algorithms suited for visually degraded environments.The LAOT is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LSW-CVLab/LAOT</uri>

In visual tracking, developing a robust appearance model is a challenging task due to variations of object appearances such as background clutter, illumination variation and partial occlusion. In existing tracking algorithms, a target candidate is represented by linear combinations of target templates. However, the relationship between a target candidate and the corresponding target templates is nonlinear because of appearance variations. In this paper, we propose a kernelized convex hull based target representation for visual tracking. Namely, a target is represented by a nonlinear combination of target templates in a mapped higher dimensional feature space. The convex hull model can covers the target appearances that do not appear in the target templates. Experimental results demonstrate the robustness and effectiveness of the proposed tracking algorithm against several state-of-the-art tracking algorithms.

Sparse representation has been widely applied for modeling object appearance in the tracking process. Various sparsity models are coded by single feature with fixed local patches, which neglects the potential spatial information inside the appearance model. This way also gives a low robust effect on

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.

Detection and analysis of events from video sequences is probably one of the most important research issues in computer vision and pattern analysis society. Before, however, applying methods and tools for analyzing actions, behavior or events, we need to implement robust and reliable tracking algorithms able to automatically monitor the movements of many objects in the scene regardless of the complexity of the background, existence of occlusions and illumination changes. Despite the recent research efforts in the field of object tracking, the main limitation of most of the existing algorithms is that they are not enriched with automatic recovery strategies able to re-initialize tracking whenever its performance severely deteriorates. This is addressed in this paper by proposing an automatic tracking recovery tool which improves the performance of any tracking algorithm whenever the results are not acceptable. For the recovery, non-linear object modeling tools are used which probabilistically label image regions to object classes. The models are also time varying. The first property is implemented in our case using concepts from functional analysis which allow parametrization of any arbitrary non-linear function (with some restrictions on its continuity) as a finite series of known functional components but of unknown coefficients. The second property is addressed by proposing an innovative algorithm that optimally estimates the non-linear model at an upcoming time instance based on the current non-linear models that have been already approximated. The architecture is enhanced by a decision mechanism which permits verification of the time instances in which tracking recovery should take place. Experimental results on a set of different video sequences that present complex visual phenomena (full and partial occlusions, illumination variations, complex background, etc) are depicted to demonstrate the efficiency of the proposed scheme in proving tracking in very difficult visual content conditions. Additionally, criteria are proposed to objectively evaluate the tracking performance and compare it with other strategies.

Research on object tracking has been an active field because of its fundamental roles in surveillance and monitoring. In this paper, a new adaptive algorithm for fast target tracking based on hierarchical block matching is proposed. The improvement of the algorithm lies in three aspects. First, the hierarchical pyramid method is applied to enhance the search efficiency of the local features. In addition, at different pyramid level, an adaptive searching strategy is adopted according to the number of candidate features. Second, since image blocks make full use of local characters of features, the structure similarity method, associated with the relation coefficient in statistics, can be exploited to design a new matching criteria, which provides more precise matching results, especially in the complex scenes such as the change of illumination and partial occlusion. Lastly, traditional algorithms often track the object through adjacent frames, whereas our algorithm will search two frames with a variable interval on the basis of matching results. The intervals are obtained by using the bisection method and thus the searching time can be greatly saved. Simulation results show that the proposed block matching method has better performances in the searching time and accuracy in contrast to the classic Mean-Shift target tracking algorithm and Particle Filter target tracking algorithm under the complex circumstances such as illumination variation and partial occlusion.

We propose an approach to region tracking that is derived from a Bayesian formulation. The novelty of the approach is twofold. First, no motion field or motion parameters need to be computed. This removes a major burden since accurate motion computation has been and remains a challenging problem and the quality of region tracking algorithms based on motion critically depends on the computed motion fields and parameters. The second novelty of this approach, is that very little a priori information about the region being tracked is used in the algorithm. In particular, unlike numerous tracking algorithms, no assumption is made on the strength of the intensity edges of the boundary of the region being tracked, nor is its shape assumed to be of a certain parametric form. The problem of region tracking is formulated as a Bayesian estimation problem and the resulting tracking algorithm is expressed as a level set partial differential equation. We present further extensions to this partial differential equation, allowing the possibility of including additional information in the tracking process, such as priors on the region's intensity boundaries and we present the details of the numerical implementation. Very promising experimental results are provided.

Real-time object tracking for manipulators has been applied to industrial automation such as assembly and human robot collaboration. During the tracking process, the vision sensor is used as a feedback means to the robot controller. It means that the imperfect sensing and stability issues must be carefully considered. This paper deals with the modeling of sensor physics (e.g. limited sampling rate, irregular sampling time, packet loss, noise, and latency), and realizes a globally asymptotically stable tracking controller. First, an varying rate Kalman filter is applied to estimate the markers' positions and the corresponding error co-variances. Then, a maximum likelihood estimation problem is solved to estimate the transformation between the target frame and the world frame. In addition, a dynamic tracking controller is implemented to realize object tracking. The stability of the tracking controller under model uncertainties is also discussed. The proposed tracking algorithm is experimentally verified on an industrial robot.

Inspired by the recent advances in sparse theory, we propose a new sparse based object tracking framework in this paper. In order to effectively detect the target object to be tracked, we formulate and present a new sparse based representative dictionary learning algorithm and candidate selection scheme for robust object tracking. Specifically, we have used Pearson's correlation based dictionary learning algorithm to engender a highly representative dictionary. This representative dictionary is then used for modeling an adaptive appearance model for the target, which is robust to challenges like partial occlusions, illumination, clutter scenes and pose changes. We also present a new Appearance Model Structural Similarity test algorithm for meticulously identifying the target from various candidates in the consecutive frames of video sequences. Results of extensive experimentations carried out using various publicly available datasets shows that our framework produces an exorbitant performance compared to many state-of-the-art works.

In visual tracking, developing an efficient appearance model is a challenging task due to the influence of various factors, such as illumination variation, occlusion, background clutter, and so on. Existing tracking algorithms use appearance samples from previous frames to form a template set upon which target appearance models are built. However, these appearance models are data-dependent, so they may be corrupted by significant appearance variation. It is difficult to update the templates in challenging environments. In this paper, we propose a robust visual tracking algorithm with an adaptive appearance model using a point-to-set metric learning technique. To do this, we first model a target representation using a set of target templates and a regularized affine hull (RAH) spanned by the target templates. Then, we learn a point-to-set distance metric, which is incorporated into the optimization process to obtain an adaptive target representation. The RAH model covers unseen target appearances by affine combinations of the target templates. Based on the proposed target appearance model, we design an effective template update scheme by adjusting the weights of the target templates. Experimental results on challenging video sequences with comparisons to several state-of-the-art tracking algorithms demonstrate the effectiveness and robustness of the proposed tracking algorithm.

In this paper, we propose a multi-task object tracking algorithm, which is based on an incremental subspace learning method and is denoted as the Multi-Task Gaussian-Laplacian Regression Tracker (MGLRT). Firstly, we model the candidate targets as a mutli-task linear regression by PCA basis vectors. Secondly, considering the complexity of the real noise in a tracking system, we model the noise as an addition of Gaussian and Laplacian noise. In the corresponding optimization problem, we denote by ||E||2,1 and ||S||1,1 the Multi-Task Gaussian and Laplacian noise term. In addition, since the candidate targets are densely sampled around the current target state, the representations will be linearly dependent. Therefore, in order to improve the performance of our tracker, the representations matrix is expected to be low-rank. Finally, we test our tracking algorithm on challenging videos that include partial occlusion, illumination variation, pose change, background clutter and motion blur. Experimental results show that our proposed approach performs favorably against several state-of-the-art trackers.

Visual tracking is an open issue in computer vision. Designing an robust tracking algorithm is a challenging problem due to complicated appearance variations, such as partial occlusion, illumination variations, fast motion and out-of-plane rotation. Some tracking algorithms use a set of templates to represent a target candidate. Such representations are not robust to drastic appearance variations. In order to alleviate the influence of appearance variations and outliers, we propose a novel tracking algorithm based on a robust Bayesian matrix factorization, where sample data is decomposed into a basis matrix and a coefficient matrix. The sample data is consist of previous tracking results. A column vector is stacked by a tracking result in a frame. A target candidate is represented by a linear combinations of the columns in the basis matrix. The matrix factorization can capture the correlation between different features in sample data. Also the appearance variations in tracking processing can be learnt in matrix factorization. Experimental results demonstrate the appearance models is effective and the proposed tracker in a particle filter tracking framework is robust to outliers and complicated appearance variations.

In this paper, a new method based on estimation and line-of-sight (LOS) detection of individual time-of-arrival (TOA) measurement and Kalman filter (KF) is proposed to track a moving target in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments. In the proposed tracking algorithm, TOA measurements collected by multiple stationary sensors in a wireless sensor network are used. First, a pseudo-measured position is calculated by choosing the point along the circle defined by a given TOA measurement which has the shortest distance to the predicted position of a moving target. The pseudo-measured position is shown to be an approximately unbiased estimate of the true position of the target. Second, each pseudo-measured position calculated is passed to a detector to be identified as either LOS or NLOS. The average of all the selected LOS pseudo-measured positions is then used as a new pseudo-measurement for the KF to track the moving target. Unlike all the existing target tracking algorithms in mixed LOS/NLOS environments, the proposed tracking algorithm is able to perform target tracking even with just one LOS TOA measurement at a given time instance without prior information of the NLOS noise which may be difficult to obtain in practice. Another advantage of the proposed tracking algorithm is its computational efficiency. Simulation results show that the proposed tracking algorithm performs better than some recent tracking algorithms, particularly in severe mixed LOS/NLOS environments.

The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations such as motion blur and lighting changes. A tracking algorithm based on color statistical features has a good effect on the rotation and translation of objects. The Staple algorithm combines the two algorithms to track using complementary fusion, but it also does not handle the occlusion and other issues well. In this paper, based on the Staple algorithm, the average peak correlation energy (APCE) and the maximum response are introduced. The value is used as the tracking confidence, and a detector using a support vector machine (SVM) is added. When the tracking confidence is low, the target is blocked or moved violently. At this time, the detector works, and the search area is expanded around the original area for the target. At the same time, because the traditional tracking algorithm uses a fixed learning rate to update the template, this paper uses an adaptive tracking learning rate. When the tracking confidence is low, the update speed of the target model is reduced, which can effectively deal with the occlusion deformation in the tracking process. OTB100 benchmark experiments show that this method can solve the occlusion problem during target tracking. The degree of change is robust and stability.

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.