Art Trackers Research Articles

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

Hierarchical Spatiotemporal Graph Regularized Discriminative Correlation Filter for Visual Object Tracking.

Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications. It is typically formulated by estimating the target appearance model between consecutive frames. Discriminative correlation filters (DCFs) and their variants have achieved promising speed and accuracy for visual tracking in many challenging scenarios. However, because of the unwanted boundary effects and lack of geometric constraints, these methods suffer from performance degradation. In the current work, we propose hierarchical spatiotemporal graph-regularized correlation filters for robust object tracking. The target sample is decomposed into a large number of deep channels, which are then used to construct a spatial graph such that each graph node corresponds to a particular target location across all channels. Such a graph effectively captures the spatial structure of the target object. In order to capture the temporal structure of the target object, the information in the deep channels obtained from a temporal window is compressed using the principal component analysis, and then, a temporal graph is constructed such that each graph node corresponds to a particular target location in the temporal dimension. Both spatial and temporal graphs span different subspaces such that the target and the background become linearly separable. The learned correlation filter is constrained to act as an eigenvector of the Laplacian of these spatiotemporal graphs. We propose a novel objective function that incorporates these spatiotemporal constraints into the DCFs framework. We solve the objective function using alternating direction methods of multipliers such that each subproblem has a closed-form solution. We evaluate our proposed algorithm on six challenging benchmark datasets and compare it with 33 existing state-of-the art trackers. Our results demonstrate an excellent performance of the proposed algorithm compared to the existing trackers.

Rotation-aware dynamic temporal consistency with spatial sparsity correlation tracking

Recently, discriminative correlation filter (DCF)-based trackers have been widely applied to visual tracking. However, a significant problem of DCF-based trackers is that the model uses the fixed patterns of temporal modeling and fails to suppress the distractive features. To obviate the issue, we propose the dynamic temporal consistency with spatial sparsity correlation filter. The dynamics refers to the adaptive temporal consistency hidden in the response maps and dynamic lasso constraint moderated by the prior knowledge. Unlike the classical temporal modeling method applied to filter model, we exploit the consistency of the response maps to perceive adaptive temporal continuity modeling to enable the filter to have self-regularized ability. Temporal modeling and spatial sparsity are incorporated in a unified optimization learning model and optimized together with ADMM algorithm and Sherman-Morrison formula. In particular, the tracking performance is hindered by the weak capacity to rotation variation. For the sake of estimating the rotation angle accurately to maintain rotation invariance, we explore the coarse-to-fine rotation estimation module. The coarse rotation is supported by the angle pool and the part-based tracker. By introducing the connected hyper ellipse fitting strategy to eliminate fake distractors to ensure a pure target region, the fine level attempts to achieve the optimal rotation angle with the minimum second order statistical bias. The design enables the filter to train with the recovered rotated image instead of axis-aligned bounding box, which attributes to alleviating the impact of ambiguous region and concentrating on the interested target. Extensive experimental results validate the superiority of the proposed method against other state-of-the art trackers and exhibit a remarkable generality in the rotated challenging scenarios. • Dynamic temporal modeling can make the filter highlight the ever-changing region. • Dynamic temporal consistency modeling and spatial sparsity are incorporated in a unified optimization learning model. • The connected hyper ellipse fitting strategy tries to achieve the optimal rotation angle. • Comprehensive experiments have fully demonstrated the superior performance of our proposed method.

Variational Inference and Learning of Piecewise Linear Dynamical Systems.

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single linear behavior. Alternatively, it is possible to consider a piecewise linear model which, combined with a switching mechanism, is well suited when several modes of behavior are needed. Nevertheless, switching dynamical systems are intractable because their computational complexity increases exponentially with time. In this article, we propose a variational approximation of piecewise linear dynamical systems. We provide full details of the derivation of two variational expectation-maximization algorithms: a filter and a smoother. We show that the model parameters can be split into two sets: static and dynamic parameters, and that the former parameters can be estimated offline together with the number of linear modes, or the number of states of the switching variable. We apply the proposed method to the head-pose tracking, and we thoroughly compare our algorithms with several state of the art trackers.

SiamMN: Siamese modulation network for visual object tracking

Visual object tracking methods based on Siamese network are often difficult to distinguish objects with the same semantic or similar appearance as tracking target in tracking process due to the lack of discriminating strategies for the confusing objects. We propose a visual object tracking method based on Siamese modulation network. It takes the given bounding box in the target frame and the current frame as input, and fuses these multi-layer convolutional features to obtain more target appearance information of bounding box and the current frame. The feature modulator generates feature modulation vector based on the given bounding box to enhance visual appearance information of target instance in multi-layer feature of the current frame, so as to make target instance obtain higher score in response map of region proposal network, and thus realize target instance-specific tracking task. Experiments on two public benchmark datasets, OTB2015 and VOT2018, show that the proposed tracker has a competitive performance among other state-of-the art trackers.

Re-identification framework for long term visual object tracking based on object detection and classification

In this paper, we address the problem of long-term visual object tracking and we present an efficient real-time single object tracking system suitable for integration in autonomous platforms that need to encompass intelligent capabilities. We propose a novel long-term tracking framework for classification based re-detection and tracking, that incorporates state estimation, object re-identification and automated management of tracking and detection results. Our method integrates a novel object re-identification technique which efficiently filters a number of detection candidates and systematically corrects the tracking results. Through extensive experimental validation on the UAV123, UAV20L and TLP datasets, we demonstrate the effectiveness of the proposed system and its advantage over several state-of-the art trackers. The results furthermore highlight the proposed tracker’s ability to handle challenges arising from real-world and long-term scenarios, such as variations in pose, scale, occlusions and out-of-view situations. Furthermore, we propose a variant that is suitable for deployment on autonomous robots, such as Unmanned Aerial Vehicles.

A Robust Tracking-by-Detection Algorithm Using Adaptive Accumulated Frame Differencing and Corner Features.

We propose a tracking-by-detection algorithm to track the movements of meeting participants from an overhead camera. An advantage of using overhead cameras is that all objects can typically be seen clearly, with little occlusion; however, detecting people from a wide-angle overhead view also poses challenges such as people’s appearance significantly changing due to their position in the wide-angle image, and generally from a lack of strong image features. Our experimental datasets do not include empty meeting rooms, and this means that standard motion based detection techniques (e.g., background subtraction or consecutive frame differencing) struggle since there is no prior knowledge for a background model. Additionally, standard techniques may perform poorly when there is a wide range of movement behaviours (e.g. periods of no movement and periods of fast movement), as is often the case in meetings. Our algorithm uses a novel coarse-to-fine detection and tracking approach, combining motion detection using adaptive accumulated frame differencing (AAFD) with Shi-Tomasi corner detection. We present quantitative and qualitative evaluation which demonstrates the robustness of our method to track people in environments where object features are not clear and have similar colour to the background. We show that our approach achieves excellent performance in terms of the multiple object tracking accuracy (MOTA) metrics, and that it is particularly robust to initialisation differences when compared with baseline and state of the art trackers. Using the Online Tracking Benchmark (OTB) videos we also demonstrate that our tracker is very strong in the presence of background clutter, deformation and illumination variation.

Human motion tracking under indoor and outdoor surveillance system

This paper gives an overview of the various potential applications involved in human motion tracking. Also, a review of some relevant algorithms in this area are summarised based on three main components: the human target extraction, the features extraction, and the motion model. In addition, a proposed generative and discriminative human tracking method is presented. This proposed method is based on the hybridisation of Kalman filter (KF) and the support vector machines (SVMs). Numerous experiments illustrate the effectiveness of the proposed method against several state of the art trackers. These experiments are applied on several real world image sequences with various challenges that make the tracker vulnerable which are: partial and full occlusions, illumination changes, scale variation, out-of-plane, rotation, motion blur, low resolution, deformation, fast motion and background clutter.

Robust object tracking based upon adaptive multi-cue integration for video surveillance

It was well argued in literature that integrating multi-cue increases accuracy and robustness of visual tracking. Although, multi-cues object tracking using singlemodal or multimodal was explored by some of researchers, it still remains an open challenge to fuse multi-cue from different modularity under dynamic environment conditions. The aim of this research paper is to introduce a novel multi-cue object tracking framework using particle filter. In particle filter framework, our approach integrates cues while evaluating each particle instead of primitive approach of deciding cues performance in current frame based upon either a few present particles or previous state particles. First, we model our multi-cue tracking framework using Shafer's model and multi-cue data is combined using Conjunctive combination rules. The partial and total conflict among cues at particle level is redistributed efficiently using Proportional Conflict Redistribution (PCR-5) rules. In proposed model, automatic suppression/boosting of particles along with online conflict resolving facilitate resampling process for efficiently handling of particle degeneracy. Most importantly, compared to other state-of-art trackers, our proposed algorithm can handle more efficiently various dynamic environments conditions such as partial or full occlusion, illumination changes, weather, and visibility. In this manuscript, we demonstrate our proposed adaptive multi-cue fusion model on challenging benchmark video and thermal sequences and compare tracking results of our tracker with state-of- art trackers.

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,11An earlier brief version of the paper has appeared in ICIP'13 (R. Venkatesh Babu and P. Priti, “Interest points based object tracking via sparse representation”, in proceedings of International Conference on Image Processing (ICIP), Melbourne, Australia, 2013). we propose a robust interest point based tracker in l1 minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l1 minimization problem.In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos.

Co-trained generative and discriminative trackers with cascade particle filter

Visual tracking is a challenging problem, as the appearance of an object may change due to viewpoint variations, illumination changes, and occlusion. It may also leave the field of view (FOV), then reappears. In order to track and reacquire an unknown object with limited labeling data, we propose to learn these changes online and incrementally build a model that encodes all appearance variations while tracking. To address this semi-supervised learning problem, we propose a co-training framework with cascade particle filter to label incoming data continuously and online update hybrid generative and discriminative models. Each of the layers in the cascade contains one or more either generative or discriminative appearance models. The cascade manner of organizing the particle filter enables the efficient evaluation of multiple appearance models with different computational costs; thus improves the speed of the tracker. The proposed online framework provides temporally local tracking that adapts to appearance changes. Moreover, it provides an object-specific detection ability that allows to reacquire an object after total occlusion. Extensive experiments demonstrate that under challenging situations, our method has strong reacquisition ability and robustness to distracters in clutter background. We also provide quantitative comparisons to other state of the art trackers.