Template Update Research Articles

Dynamic Hand Gesture Tracking Method Based on Key Frame and Video Perceptual Hashing

Aiming at the common problems such as low tracking accuracy, robustness and poor real-time ability for the traditional tracking algorithm, a novel dynamic hand gesture tracking method based on key frame and video perceptual hashing has been proposed in this paper. Firstly, the proposed method is used to frame the video sequence and extract the key frames, in order to obtain a sequence of key frames. Secondly, the video perceptual hashing is used to extract the perceptual feature of the key frame and target template, thus the hash sequence is generated. Finally, the hamming distance is used to compare the similarities between the key frame and target template, therefore, the real-time tracking is carried out by using an online adaptive template update method to ensure the continuity of the hand gesture tracking. Experimental results show that the proposed method can realize the accurate and real-time tracking of the dynamic deformation gestures, and effectively reduce the time complexity of the algorithm, as well as ensure the stability and continuity of the moving target tracking.

Joint compressive representation for multi-feature tracking

Abstract Multi-feature tracking is an interesting but challenging task. Many previous works just consider the fusion of different features from identical spectral image or identical features from different spectral images alone, which makes them be quite distinct from each other and be difficult to be integrated naturally. To address this problem, we propose a unified multi-feature tracking framework based on joint compressive sensing in this paper. Our framework can accept features extracted from identical spectral or different spectral images, and provide the flexibility to arbitrary add or remove feature. We formulate the multi-feature tracking as a joint minimization problem of multiple l1-norms with inequality constraint of multiple l2-norms, and derive a customized augmented Lagrange multiplier algorithm to solve the minimization problem, which provides efficient object tracking with both low computational burden and high accuracy. Besides, a collaborative template update scheme induced by sparsity concentration index is developed to keep track of the most representative templates throughout the tracking procedure. Experimental results on challenging visible and infrared sequences clearly demonstrate the robustness and effectiveness of the proposed method.

Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces

With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked the development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation, while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter-based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions’ detections and alternate templates. The detection position was repositioned according to the estimated speed of target by an optical flow method, and the alternate template was stored with a template update mechanism, which is all computed at the edge. Experimental results on large-scale public benchmark data sets showed the effectiveness of the proposed method in comparison with state-of-the-art methods.

Visual tracking based on hierarchical framework and sparse representation

As the main challenge for object tracking is to account for drastic appearance change, a hierarchical framework that exploits the strength of both generative and discriminative models is devised in this paper. Our hierarchical framework consists of three appearance models: local-histogram-based model, weighted alignment pooling model, and sparsity-based discriminative model. Sparse representation is adopted in local-histogram-based model layer that considers the spatial information among local patches with a dual-threshold update schema to deal with occlusion. The weighted alignment pooling layer is introduced to weight the local image patches of the candidates after sparse representation. Different from the above two generative methods, the global discriminant model layer employs candidates to sparsely represent positive and negative templates. After that, an effective hierarchical fusion strategy is developed to fuse the three models via their similarities and the confidence. In addition, three reasonable online dictionary and template update strategies are proposed. Finally, experiments on various current popular image sequences demonstrate that our proposed tracker performs favorably against several state-of-the-art algorithms.

Visual tracking based on the sparse representation of the PCA subspace

We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

Robust object tracking with RGBD-based sparse learning

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.

Visual tracking with genetic algorithm augmented logistic regression

In this paper, a genetic algorithm (GA) augmented logistic regression tracker is proposed. We enhance our tracker in three aspects. Firstly, a novel concept of intelligent motion model based on GA and particle filter is proposed to handle the partial occlusion, object drift and fast object motion changes during tracking. Secondly, the powerful and efficient features including FHOG and Lab are integrated to further boost the tracking performance. Thirdly, mechanism of dynamic update and choice mechanism of positive and negative templates are introduced to better adapt to the appearance changes. Extensive experimental results on the Object Tracking Benchmark dataset show that the proposed tracker performs favorably against state-of-the-art methods in terms of accuracy and robustness.

Patch-based visual tracking with online representative sample selection

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

Object tracking based on online representative sample selection via non-negative least square

In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination of representative samples and propose a novel approach to select representative samples based on the coefficient constrained model. We formulate the objective function into a non-negative least square problem and obtain the solution utilizing standard non-negative least square. The experimental results show that the observation module of our approach outperforms several other observation modules under the same feature and motion module, such as support vector machine, logistic regression, ridge regression and structured outputs support vector machine.

A Cluster Tracking Method Based on Cam-shift

The classic Mean-Shift algorithm lacks the necessary template update, because window size remains the same in tracking process, tracking will fail when the template scale change, track will be ineffective when the template is faster, the feature of histogram seems simple in the object color characteristic described aspects and lacks space information. This paper presents a Cam-Shift clustering algorithm, regarding the centroid position of the moving object which is detected as the first iteration of the input frame, narrowing the visual search range, shortening the matching time. Through the template update of color model and cluster, the algorithm resists the interference of light, strain, shelter and achieving more precise moving object motion parameter estimation and tracking results than the existing algorithms to a certain extent. Experiment is used to verify each algorithm in this article. Selecting aerial image sequence multiple moving vehicle object to study, results are capable of getting sustained and effective track to multiple moving objects in image sequences.

Robust visual tracking using information theoretical learning

This paper presents a novel online object tracking algorithm with sparse representation for learning effective appearance models under a particle filtering framework. Compared with the state-of-the-art l1 sparse tracker, which simply assumes that the image pixels are corrupted by independent Gaussian noise, our proposed method is based on information theoretical Learning and is much less sensitive to corruptions; it achieves this by assigning small weights to occluded pixels and outliers. The most appealing aspect of this approach is that it can yield robust estimations without using the trivial templates adopted by the previous sparse tracker. By using a weighted linear least squares with non-negativity constraints at each iteration, a sparse representation of the target candidate is learned; to further improve the tracking performance, target templates are dynamically updated to capture appearance changes. In our template update mechanism, the similarity between the templates and the target candidates is measured by the earth movers' distance(EMD). Using the largest open benchmark for visual tracking, we empirically compare two ensemble methods constructed from six state-of-the-art trackers, against the individual trackers. The proposed tracking algorithm runs in real-time, and using challenging sequences performs favorably in terms of efficiency, accuracy and robustness against state-of-the-art algorithms.

Visual tracking with conditionally adaptive multiple template update scheme for intricate videos

Tracking of moving objects in real-time scenes is a challenging research problem in computer vision. This is due to incessant live changes in the object features, background, occlusions, and illumination deviations occurring at different instances in the scene. With the objective of tracking visual objects in intricate videos, this paper presents a new color-independent tracking approach, the contributions of which are threefold. First, the illumination level of the sequences is maintained constant using fast discrete curvelet transform. Fisher information metric is calculated based on a cumulative score by comparing the template patches with a reference template at different timeframes. This metric is used for quantifying distances between the consecutive frame histogram distributions. Then, a novel iterative algorithm called conditionally adaptive multiple template update is proposed to regulate the object templates for handling dynamic occlusions effectively. The proposed method is evaluated on a set of extensive challenging benchmark datasets. Experimental results in terms of Center Location Error (CLE), Tracking Success Score (TSS), and Occlusion Success Score (OSS) show that the proposed method competes well with other relevant state-of-the-art tracking methods.

Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo.

A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Monte Carlo method and determine the target location by the correlation filter simultaneously. By analyzing the response map of the target region, the completeness of the target can be measured by the peak-to-sidelobe rate (PSR), i.e., the lower the PSR, the more likely the target is being occluded. A strict template update strategy is designed to accommodate the appearance change and avoid template corruption. If the occlusion occurs, a retained scheme is allowed and the tracker refrains from drifting away. Additionally, the feature integration is incorporated to guarantee the robustness of the proposed approach. The experimental results show that our method outperforms other state-of-the-art trackers in terms of both the distance precision and overlap precision on the publicly available TB-50 dataset.

A two-stage visual tracking algorithm using dual-template

Template matching and updates are crucial steps in visual object tracking. In this article, we propose a two-stage object tracking algorithm using a dual-template. By design, the initial state of a target can be estimated using a prior fixed template at the first stage with a particle-filter-based tracking framework. The use of prior templates maintains the stability of an object tracking algorithm, because it consists of invariant and important features. In the second step, a mean shift is used to gain the optimal location of the object with the stage update template. The stage template improves the ability of target recognition using a classified update method. The complementary of dual-template improves the quality of template matching and the performance of object tracking. Experimental results demonstrate that the proposed algorithm improves the tracking performance in terms of accuracy and robustness, and it exhibits good results in the presence of deformation, noise and occlusion.

Enhanced template update: Application to keystroke dynamics

With the increasing number of activities being performed using computers, there is an ever growing need for advanced authentication mechanisms like biometrics. One efficient and low cost biometric modality is keystroke dynamics, which attempts to recognize users by their typing rhythm. It has been shown that the biometric features may undergo changes over time, which can reduce the predictive performance of the biometric system. Template update adapts the user model to deal with these changes and, therefore, decreases the predictive performance loss. Most of the studies in the literature only take into account samples classified as genuine to perform adaptation. This paper extends this common approach by proposing an original framework to make use of samples classified as impostors, too. This new approach, named Enhanced Template Update, uses all collected unlabeled samples to support the adaptation process. According to our experimental results, this new approach can improve the predictive performance when compared to current methods depending on the scenario. Some improvements on the visualization of results over time are also proposed during the analysis performed in this study. Although the proposed approach is evaluated on keystroke dynamics, it could also be applied to other biometric modalities.

Automatic Identification of the Cardiac Rest Period Using Template Updating for Magnetic Resonance Coronary Angiography

Automatic Identification of the Cardiac Rest Period Using Template Updating for Magnetic Resonance Coronary Angiography

Adaptive Appearance Modeling with Point-to-Set Metric Learning for Visual Tracking

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.

Efficient online and offline template update mechanisms for speaker recognition

Sample variations are one of the main problems associated with speaker recognition. Most approaches use multiple templates in the gallery database. But, this requires enormous memory space. In order to minimize classification errors and intra-class variations, adaptive online and offline template update methods using vector quantization (VQ) and Gaussian mixture model (GMM) are proposed. Online and offline feature update as well as model update techniques are considered here. Feature update utilizes the vector quantization approach, while Gaussian mixture model approach is considered for model updating. The proposed methods automatically update the feature (model) in accordance with the biometric sample variations over time and they continually adapt the templates (user model) based on semi-supervised learning strategies. Experiments with 50 subjects reveal that the proposed template update strategies, improve the recognition accuracy and reduce the classification errors for voice recognition systems, even under sample variations.

A TSR Visual Servoing System Based on a Novel Dynamic Template Matching Method.

The so-called Tethered Space Robot (TSR) is a novel active space debris removal system. To solve its problem of non-cooperative target recognition during short-distance rendezvous events, this paper presents a framework for a real-time visual servoing system using non-calibrated monocular-CMOS (Complementary Metal Oxide Semiconductor). When a small template is used for matching with a large scene, it always leads to mismatches, so a novel template matching algorithm to solve the problem is presented. Firstly, the novel matching algorithm uses a hollow annulus structure according to a FAST (Features from Accelerated Segment) algorithm and makes the method be rotation-invariant. Furthermore, the accumulative deviation can be decreased by the hollow structure. The matching function is composed of grey and gradient differences between template and object image, which help it reduce the effects of illumination and noises. Then, a dynamic template update strategy is designed to avoid tracking failures brought about by wrong matching or occlusion. Finally, the system synthesizes the least square integrated predictor, realizing tracking online in complex circumstances. The results of ground experiments show that the proposed algorithm can decrease the need for sophisticated computation and improves matching accuracy.

An accurate slap fingerprint based verification system

A slap fingerprint based verification system generally is highly secure. But it cannot handle the problems of temporal deformation and pose variation. In this paper, a template update algorithm has been proposed to ensure better performance of the system. During verification, each single fingerprint is matched and matching scores of all fingerprints are fused using an adaptive score level fusion. The performance of the proposed system has been evaluated on a challenging database containing 1800 slap-images acquired from 150 subjects. Experimental results show that the accuracy is increased by more than 3.5%.