Tracking Error Accumulation Research Articles

Integral Windup Resetting Enhancement for Sliding Mode Control of Chemical Processes with Longtime Delay

The effects of the windup phenomenon impact the performance of integral controllers commonly found in industrial processes. In particular, windup issues are critical for controlling variable and longtime delayed systems, as they may not be timely corrected by the tracking error accumulation and saturation of the actuators. This work introduces two anti-windup control algorithms for a sliding mode control (SMC) framework to promptly reset the integral control action in the discontinuous mode without inhibiting the robustness of the overall control system against disturbances. The proposed algorithms are intended to anticipate and steer the tracking error toward the origin region of the sliding surface based on an anti-saturation logistic function and a robust compensation action fed by system output variations. Experimental results show the effectiveness of the proposed algorithms when they are applied to two chemical processes, i.e., (i) a Variable Height Mixing Tank (VHMT) and (ii) Continuous Stirred Tank Reactor (CSTR) with a variable longtime delay. The control performance of the proposed anti-windup approaches has been assessed under different reference and disturbance changes, exhibiting that the tracking control performance in the presence of disturbances is enhanced up to 24.35% in terms of the Integral Square Error (ISE) and up to 88.7% regarding the Integral Time Square Error (ITSE). Finally, the results of the proposed methodology demonstrated that the excess of cumulative energy by the actuator saturation could reduce the process resources and also extend the actuator’s lifetime span.

An improved moving object tracking algorithm based on CAMSHIFT

There is a problem that Camshift algorithm relies too much on color as a criterion, in order to solve it, this paper presented an improved moving object tracking algorithm which combined Kalman filter predictor with Camshift algorithm. Firstly, the object model was built by Camshift Algorithm, and the centroid coordinates of the moving objectwere extracted, then the position of the object in the next frame was predicted by using Kalman filter, and finally, the value was used as the centroid coordinates of the object searched by Camshift algorithm in the next frame, proceeding with target match. The simulation results showed that this method can effectively solve the problem of target loss caused by the similar color between the target and the back. The new algorithm can effectively reduce the tracking error accumulation in the tracking process, greatly reduce the template drift, shorten the tracking time, and can have a better practicability and real-time performance.

Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow.

Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

Robust contour tracking in ultrasound tongue image sequences

ABSTRACTA new contour-tracking algorithm is presented for ultrasound tongue image sequences, which can follow the motion of tongue contours over long durations with good robustness. To cope with missing segments caused by noise, or by the tongue midsagittal surface being parallel to the direction of ultrasound wave propagation, active contours with a contour-similarity constraint are introduced, which can be used to provide ‘prior’ shape information. Also, in order to address accumulation of tracking errors over long sequences, we present an automatic re-initialization technique, based on the complex wavelet image similarity index. Experiments on synthetic data and on real 60 frame per second (fps) data from different subjects demonstrate that the proposed method gives good contour tracking for ultrasound image sequences even over durations of minutes, which can be useful in applications such as speech recognition where very long sequences must be analyzed in their entirety.

DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking.

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework.

MO‐DE‐210‐05: Improved Accuracy of Liver Feature Motion Estimation in B‐Mode Ultrasound for Image‐Guided Radiation Therapy

Purpose:In similarity‐measure based motion estimation incremental tracking (or template update) is challenging due to quantization, bias and accumulation of tracking errors. A method is presented which aims to improve the accuracy of incrementally tracked liver feature motion in long ultrasound sequences.Methods:Liver ultrasound data from five healthy volunteers under free breathing were used (15 to 17 Hz imaging rate, 2.9 to 5.5 minutes in length). A normalised cross‐correlation template matching algorithm was implemented to estimate tissue motion. Blood vessel motion was manually annotated for comparison with three tracking code implementations: (i) naive incremental tracking (IT), (ii) IT plus a similarity threshold (ST) template‐update method and (iii) ST coupled with a prediction‐based state observer, known as the alpha‐beta filter (ABST).Results:The ABST method produced substantial improvements in vessel tracking accuracy for two‐dimensional vessel motion ranging from 7.9 mm to 40.4 mm (with mean respiratory period: 4.0 ± 1.1 s). The mean and 95% tracking errors were 1.6 mm and 1.4 mm, respectively (compared to 6.2 mm and 9.1 mm, respectively for naive incremental tracking).Conclusions:High confidence in the output motion estimation data is required for ultrasound‐based motion estimation for radiation therapy beam tracking and gating. The method presented has potential for monitoring liver vessel translational motion in high frame rate B‐mode data with the required accuracy.This work is support by Cancer Research UK Programme Grant C33589/A19727.

A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.

Visual Tracking Using Combining Motion Constraint Model and Online Multiple Instance Boost Random Ferns

Video object tracking is essential algorithm for computer vision applications. An object tracking algorithm using combining motion constraints model and online multiple instance boost random ferns is proposed, which use IIR filter to obtain online learning for random ferns, and the random ferns are selected by online multiple instance boosting to construct classifier of online multiple instance boost random ferns. To reduce effects of tracking error accumulation, object motion constraint model is constructed to constrain the results classified by online multiple instance boost random ferns to locate object correctly, and construct positive and negative set to online update the classifier. The experiment shows that the proposed method achieves competitive detection results, which are comparable with state-of-the-art methods.

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the `tracking error accumulation problem'. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel `twin-pipeline' training framework to find the latent semantic topics of `moving people', the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.

Fast-moving target tracking based on mean shift and frame-difference methods

Abstract: The mean shift tracker has difficulty in tracking fastmoving targets and suffers from tracking error accumulation prob-lem. To overcome the limitations of the mean shift method, a newapproach is proposed by integrating the mean shift algorithm andframe-difference methods. The rough position of the moving tar-get is first located by the direct frame-difference algorithm andthree-frame-difference algorithm for the immobile camera scenesand mobile camera scenes, respectively. Then, the mean shiftalgorithm is used to achieve precise tracking of the target. Severaltracking experiments show that the proposed method can effec-tively track first moving targets and overcome the tracking erroraccumulation problem. Keywords: mean shift, frame-difference method, target tracking,computer vision. DOI: 10.3969/j.iss.1004-4132.2011.04.006 1. Introduction Mean shift is a nonparametric kernel density estima-tor, which is based on the color kernel density appear-ance change. Fukunaga et al. [1] first proposed themean shift algorithm for clustering data in 1975. LaterCheng [2] introduced it to the image-processing com-munity in 1995. A remarkable work on mean shift al-gorithm for image segmentation and tracking is done in[3–7]. Later, many variants of the mean shift algorithmwereproposedfordifferentapplications[8–12]. Themeanshift tracker performswell with relatively low-speed mov-ing targets. However, its performance is not guaran-teed for fast-moving targets [6,9,10,13–15]. Because themean shift algorithm is an iterative algorithm, it suffersfromthetrackingerroraccumulationproblemandlacksanerror-eliminating mechanism. The Kalman filter has been