Robust Object Tracking Research Articles

A novel approach for multi-cue feature fusion for robust object tracking

Object tracking is a significant problem of computer vision due to challenging environmental variations. Single cue appearance model is not sufficient to handle the variations. To this end, we propose a multi-cue tracking framework in which complementary cues namely, LBP and HOG were exploited to develop a robust appearance model. The proposed feature fusion captures the high-level relationship between the features and diminishes the low-level relationship. Transductive reliability is also integrated at each frame to make tracker adaptive with the changing environment. In addition, K-Means based classifier creates clear and concise boundary between positive and negative fragments which are further used to update the reference dictionary. This adaptation strategy prevents the erroneous updation of the proposed tracker during background clutters, occlusion, and fast motion. Qualitative and quantitative analysis on challenging video sequences from OTB-100 dataset, VOT dataset and UAV123 reveal that the proposed tracker performs favorably against 13 others state-of-the-art trackers.

Visual object tracking using sparse context-aware spatio-temporal correlation filter

This paper presents a novel sparse context-aware spatio-temporal correlation filter tracker (SCAST) method for robust visual object tracking. Different from the existing trackers, this paper introduce an l1 multi-scale regularization parameter-based correlation filter that reduces the boundary effect due to partial occlusions, illumination and scale variations. At each iteration, the l1 regularization parameter is updated through spatial knowledge of each correlation filter coefficient. Besides, the contextual information acquired from the target region can lead to determining the accurate localization of the target. Moreover, contextual information has combined with spatio-temporal factor to achieve the better performance. Further, an objective function is designed with system constraints to ensure the applicability of the model and the optimal solution is derived by utilizing the alternating direction method of multiplier, which leads to low computational cost. Finally, the feasibility and superiority of proposed tracker algorithm is evaluated through three benchmark dataset: OTB-2013, OTB-2015, and TempleColor-128.

Research on the improvement of vision target tracking algorithm for Internet of things technology and Simple extended application in pellet ore phase

With the extensive application of Internet of things technology and visual object tracking technology in various neighborhoods, the traditional visual object tracking algorithm can no longer meet the needs of Internet of things. The Internet of things with its powerful perception, multi-information transmission ability and super intelligent processing ability force the traditional visual object tracking algorithm to innovate and improve. In order to improve the tracking algorithm’s ability to cope with complex and drastic changes in the target’s appearance, in this paper, an accurate and robust visual object tracking algorithm (visual object tracking) based on K-means clustering algorithm on the integrated model ENS is studied. The tracking target is divided into several sample packets, and the size of the sample packet is compressed to match different weights of the filter in frequency domain. Finally, a more accurate and robust filter is built to realize the tracking of the target. Experimental simulation results show that the improved visual object tracking algorithm ENS-CSK approximately conforms to two-dimensional gaussian distribution in tracking effect, and the tracking success rate and accuracy are higher than the AdaBoost algorithm and KCF algorithm mentioned in the paper. Therefore, the improved visual object tracking algorithm ENS-CSK algorithm in this paper can better match the visual object tracking under the Internet of things technology. Therefore, it can be extended to the process of ore phase transformation of pellet under continuous calcination with varying temperature. Thus, the pressure resistance of pellets at different calcining temperatures can be predicted more accurately.

SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins1

Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning

Object tracking is a critical research in computer vision and has attracted significant attention over the past few years. However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter. This paper proposes a robust and effective object tracking algorithm, called MCM, which combines compressive sensing and online multiple instance learning in a multi-classifier fusion framework. In this framework, we integrate the different discriminative classifiers by learning the varied and compressed feature vectors based on different random projection matrices. And then an improved online multiple instance learning mechanism SMILE is adopted, which introduces the relative similarity to select and weight the instances in the positive bag. The experiments show that the proposed algorithm can improve the performance of object tracking on the challenging video sequences.

A Bottom-Up and Top-Down Integration Framework for Online Object Tracking

Robust online object tracking entails integrating short-term memory based trackers and long-term memory based trackers in an elegant framework to handle structural and appearance variations of unknown objects in an online manner. The integration and synergy between short-term and long-term memory based trackers have yet studied well in the literature, especially in pre-training free settings. To address this issue, this paper presents a bottom-up and top-down integration framework. The bottom-up component realizes a data-driven approach for particle generation. It exploits a short-term memory based tracker to generate bounding box proposals in a new frame. In the top-down component, this paper presents a graph regularized sparse coding scheme as the long-term memory based tracker. The over-complete bases for sparse coding are composed of part-based representations learned from earlier tracking results and new observations to form a space with rich temporal context information. A particle graph is computed whose nodes are the bottom-up discriminative particles and edges are formed on-the-fly in terms of appearance and spatial-temporal similarities between particles. The particle graph induces a regularization term in optimizing the sparse coding coefficients for bottom-up particles. In experiments, the proposed method is tested on the widely used OTB-100 benchmark and the VOT2016 benchmark with better performance obtained than baselines including deep learning based trackers. In addition, the outputs from the top-down sparse coding are potentially useful for downstream tasks such as action recognition, multiple-object tracking, and object re-identification.

Partial tracking method based on siamese network

Robust object tracking is still a challenging task in the field of computer vision and has application value in many fields such as automatic driving, human–computer interaction and robot visual navigation. More and more researchers are devoted to researching more accurate object tracking methods. How to better deal with occlusion and deformation has always been the difficult challenges in the object tracking field, and the existing methods cannot solve these problems well. In this regard, we propose a novel, effective and portable module called part-based tracking and assembly (PTA), which is added to the fully convolutional siamese networks to divide the exemplar feature map into several parts. Each part is separately tracked, and then the tracking results of all parts are assembled to obtain the final tracking results. And the experiments on several popular tracking benchmarks show our variant trackers with the PTA module that operate at almost the same tracking speed with the original trackers and achieve superior tracking performance. Moreover, the tracking accuracy is significantly improved on the data with occlusion, deformation and background clutter. Compared with some real-time tracking methods, our variant trackers with the PTA module can achieve the state-of-the-art performance.

Parallel tracking and detection for long-term object tracking

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

Robust long-term object tracking with adaptive scale and rotation estimation

In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.

Performance Improvement of Electro Optic Search and Track System for Maritime Surveillance

Surveillance of maritime domain is absolutely vital to ensure an appropriate response against any adverse situation relating to maritime safety or security. Electro-optic search and track (EOST) system plays a vital role by providing independent search and track of potential targets in marine environment. EOST provides real-time images of objects with details, required to neutralise threats. At long range, detection and tracking capability of EOST degrades due to uncertainty in target signatures under cluttered scenario. Image quality can be improved by using suitable sensors and enhancement using the target/background signature knowledge. Robust tracking of object can be achieved by optimising the performance parameters of tracker. In the present work, improvement in the performance of EOST subsystems such as sensor, video processor and video tracker are discussed. To improve EOST performance in terms of detection and tracking, sensor selection criterion and various real time image processing techniques and their selection criteria for maritime applications have been also discussed. Resultant improvement in the quality of image recorded under marine environment has been presented.

Robust Object Tracking with Continuous Data Association based on Artificial Potential Moving Horizon Estimation

Abstract In this paper, a novel object tracking method based on moving horizon estimation (MHE) is introduced that integrates data association into numerical optimization on Bayesian state estimation. Object tracking is a classical problem that often appears in radar and vision systems for which either deterministic or probabilistic approach has been applied. While the former often encounter a failure of association, the latter avoids it by computing the expectation concerning the observations, but it requires the prior knowledge of the probabilistic distribution and may suffer from outliers. In this paper, a partially deterministic approach is built on MHE to resolve these concerns. Data association is realized by a potential function comprising the superposition of Gaussian distributions centered at each observed feature. The potential function is embedded into the stage cost of MHE; the optimal data association is conducted using the sequence of observations within the horizon. Thus robust object tracking is achieved utilizing multi-sampling data and integration of both dynamics and explicit constraints reflecting physical limitations. The advantage of the proposed method is verified through object tracking experiments on the crowded environment, where occlusion and misrecognition frequently occur.

Adaptive tracking algorithm based on target moving speed

To solve the problem of tracking failure caused by fast object movement, we proposed an adaptive tracking algorithm based on object movement speed on the basis of ECO-HC (ECO for hand-crafted feature version). First, correlation filtering method was used to obtain the distance of the maximum response point of adjacent frames. Then the speed of the object was judged by the distance of the maximum response point of the adjacent frame. Then the number of scale filter factors and interval frames updated by the template were adjusted on the basis of the object’s speed. The comparative experiment on the VOT2016 benchmarks showed that our tracker improved the accuracy and robustness of object tracking.

Robust Structural Low-Rank Tracking

Visual object tracking is an essential task for many computer vision applications. It becomes very challenging when the target appearance changes especially in the presence of occlusion, background clutter, and sudden illumination variations. Methods, that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However, because of the lack of structural constraints, these methods show performance degradation when facing the aforementioned challenges. To alleviate these limitations, we propose a new structural low-rank modeling algorithm for robust object tracking in complex scenarios. In the proposed algorithm, we consider spatial and temporal appearance consistency constraints, among the particles in the low-rank subspace, embedded in four different graphs. The resulting objective function encoding these constraints is novel and it is solved using linearized alternating direction method with adaptive penalty both in batch fashion as well as in online fashion. Our proposed objective function jointly learns the spatial and temporal structure of the target particles in consecutive frames and makes the proposed tracker consistent against many complex tracking scenarios. Results on four challenging datasets demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.

Sequential binary code selection for robust object tracking

The appearance model, which is required to be adaptive to the non-stationary environment, is the essential step in object tracking but normally suffers from imbalance between effectiveness and efficiency. In this paper, a novel method named as sequential binary code selection (SBC) is proposed to learn a set of compact binary codes for image patch representation. Using the sparse projections, the high dimensional feature can be speedily embedded into the compact binary codes with preserving both the label information and geometrical distance. By the sequential learning, the latter learned binary code which corrects the errors made by the previous codes is more discriminative to the present environment. Furthermore, though binary code selection, the most compact and least redundant hash codes from the candidate pool will be selected and kept. Experimental results illustrate the effectiveness of the SBC, as well as the state-of-the-art tracking performance of the proposed approach.

Robust Object Tracking Based on Complementary Feature Fusion and Channel Reliability

In this letter, a robust object tracking method is proposed, which is divided into two parts: robust tracker based on complementary feature fusion with channel reliability, and reliable validator based on convolutional neural network. Compared with the existing tracking algorithms, our algorithm tracks the target more robustly and accurately, and suppresses template drift problems effectively. Experimental results on the standard benchmark show that our algorithm can compete with advanced algorithms and achieve a better level.

Multi-object tracking using deformable convolution networks with tracklets updating

Many multi-object tracking methods have been proposed to solve the computer vision problem which has been attracting significant attentions because of the significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, hybrid deformable convolution neural networks with frame-pair input and deformable layers for multi-object tracking are presented. The object tracking method trained using two successive frames can predict the centers of searching windows as the locations of tracked targets to improve the accuracy and robustness of object tracking. Histogram of Oriented Gradient and CNN features are extracted as appearance features to measure similarities between objects. Kalman filter and Hungarian algorithm are used to create tracklets association which indicates the location and the trajectories of tracked targets. To solve the problem of object transformation, we construct a novel sampling strategy for off-line training with the idea of augmenting the special sampling locations in the convolution layers and pooling layers with additional offsets. Experiments on the popular challenging datasets show that the proposed tracking system performs on par with recently developed generic multi-object tracking methods, but effective for dense geometric transformation objects and with much less memory. In addition, the proposed tracking system can run in a speed of over 75 (24) fps with a GPU (CPU), much faster than most deep networks-based trackers.

Robust object tracking via online discriminative appearance modeling

A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers.

Robust Event-Based Object Tracking Combining Correlation Filter and CNN Representation.

Object tracking based on the event-based camera or dynamic vision sensor (DVS) remains a challenging task due to the noise events, rapid change of event-stream shape, chaos of complex background textures, and occlusion. To address the challenges, this paper presents a robust event-stream object tracking method based on correlation filter mechanism and convolutional neural network (CNN) representation. In the proposed method, rate coding is used to encode the event-stream object. Feature representations from hierarchical convolutional layers of a pre-trained CNN are used to represent the appearance of the rate encoded event-stream object. Results prove that the proposed method not only achieves good tracking performance in many complicated scenes with noise events, complex background textures, occlusion, and intersected trajectories, but also is robust to variable scale, variable pose, and non-rigid deformations. In addition, the correlation filter-based method has the advantage of high speed. The proposed approach will promote the potential applications of these event-based vision sensors in autonomous driving, robots and many other high-speed scenes.

Robust object tracking based on ridge regression and multi-scale local sparse coding

Recently, the technology of visual object tracking has achieved great success. However, it is still extraordinary challenging for some factors, such as scale variations, partial occlusions and so on. To deal with the problem of scale variations of the target, this paper proposes a hybrid tracking algorithm based on ridge regression and multi-scale local sparse coding. The hybrid tracking algorithm contains three parts. Firstly, a discriminative model based on two ridge regression models which include a correlation filtering ridge regression model and a color statistics ridge regression model, is used to estimate the approximate position of the target. Secondly, a multi-scale local sparse coding with particle filtering model, which combines local overlapped patches and local non-overlapped patches, is used to estimate the precise position and scale variations of the target. Thirdly, the appearance model of the target in the discriminative model based on ridge regression is updated according to the precise position and scale variations of the target in the second part. At the end, extensive experiments verify the effectiveness of the hybrid tracking algorithm in dealing with scale variations of the target.

Robust object tracking with crow search optimized multi-cue particle filter

Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon crow search optimization to overcome low performing particles detected as the outlier. Proposed outlier detection mechanism with transductive reliability achieves faster convergence of the proposed PF tracking framework. In addition, we present an adaptive fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using the proposed fusion model not only enhance the performance of the resampling method but also achieve optimal state estimation. Performance of the proposed tracker has been evaluated over benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveal that the proposed tracker not only outperforms existing solutions but also efficiently handles various tracking challenges. On average of the outcome, we achieve CLE of 10.99 and F measure of 0.683.