Mean Shift Tracking Research Articles

Six Object Tracking Algorithms: A Comparative Study

Objectives: To compare five different objects tracking algorithms performance wise with the proposed algorithm and to find out the best one among them for tracking of an object in occlusion and Background clutter condition i.e. when background contains target features. Methods/Analysis: In the proposed algorithm, the object’s position is obtained by Mean Shift tracking and then the prediction of object’s position is done through Kalman Filter. The Proposed method has Kalman Filter which consists of an adaptive system matrix and adaptive process error covariance and measurement error covariance matrices respectively. Adaptive system matrix of Kalman Filter is getting updated online depending on the quality of observation by Mean Shift algorithm and adaptive process and measurement noise covariance matrices are getting updated according to the variation in Bhattacharya Coefficient respectively. Findings: Proposed algorithm has a maximum value (0.2177 and 0.4821) of tracking efficiency metric i.e. Bhattacharya Coefficient in both video dataset among all the algorithms and it is taking less execution time (0.002761 sec and 0.005431 sec) than other Kalman filter based tracking algorithms. Applications/Improvements: Proposed algorithm is able to track the target in occlusion and background variation condition with more efficiency and less execution time than rest of the algorithms.

Scale and Orientation-Based Background Weighted Histogram for Human Tracking

The Mean Shift procedure is a popular object tracking algorithm since it is fast, easy to implement and performs well in a range of conditions. However, classic Mean Shift tracking algorithm fixes the size and orientation of the tracking window, which limits the performance when the target's orientation and scale change. In this paper, we present a new human tracking algorithm based on Mean Shift technique in order to estimate the position, scale and orientation changes of the target. This work combines moment features of the weight image with background information to design a robust tracking algorithm entitled Scale and Orientation-based Background Weighted Histogram (SOBWH). The experimental results show that the proposed approach SOBWH presents a good compromise between tracking precision and calculation time, also they validate its robustness, especially to large background variation, scale and orientation changes and similar background scenes.

Image-based structural dynamic displacement measurement using different multi-object tracking algorithms

With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.

Quad binary pattern and its application in mean-shift tracking

This paper proposes a new local texture descriptor, called quad binary pattern (QBP). Compared with local binary pattern (LBP), the QBP is with stronger robustness for feature extraction under complex scene (e.g., luminance change, similar target and background color) and with lower computational complexity. To demonstrate its effectiveness, the proposed QBP is further applied on the mean-shift tracking, in which a joint color-QBP model is developed to effectively represent the color and texture characteristics of the target region. Extensive simulation results have demonstrated that the proposed algorithm is able to improve the tracking speed and accuracy, compared with the standard mean-shift tracking and joint color-LBP model based mean-shift tracking.

Multiple cues-based active contours for target contour tracking under sophisticated background

In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a two-layer-based target appearance model that combines both discriminative pre-learned-based global layer and voting-based local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment.

Object Tracking Based on Online Semi-Supervised SVM and Adaptive-Fused Feature

Abstract In order to improve the performance of tracking, we propose a new online tracking method based on classification and adaptive fused feature. We first label a few positive and negative samples, train the classifier by the online SSSM (Semi-Supervised Support Vector Machine) learning and these labelled samples, and then locate the position of the object from the next frame according to the trained classifier. In order to adapt more of the new samples, we need to update the classifier by finding new samples with high confident value obtained by the trained classifier and add them into the online SSSM. Finally we also update the object model by the online incremental PCA (Principal Component Analysis) because of background clutter, heavy occlusion and complicated object appearance changes. Compared with the basic mean shift tracking and the ensemble tracking method, experimental results show that our tracking method is able to effectively handle heavy occlusion and background clutter in some challenge videos including some thermal videos.

Robust mean shift tracking based on refined appearance model and online update

In this paper, a robust mean shift tracking algorithm based on refined appearance model (RAM) and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model to improve tracking precision and design an online update strategy to adjust to the appearance variation. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model construction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to distinguish the foreground from background. A weighted vector is then obtained based on the classified foreground from background, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on some challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance. A further comprehensive experiment on OTB2013 demonstrates that the proposed tracking algorithm outperforms the state-of-the-art works in most cases.

Integration of colour and uniform interlaced derivative patterns for object tracking

Object tracking as a branch of computer vision plays a key role in the intelligent video surveillance. In recent years, the mean shift algorithm due to its simplicity and robustness has attracted great attention for tracking the object by using a colour model, while only using the colour causes the error in some cases of tracking such as illumination variations and so on. Consequently, the authors proposed an enhanced mean shift tracking algorithm. First, they presented a new texture-based target representation by a modified version of the interlaced derivative pattern, which considers spatial dependencies between pixels. Second, an improvement for the mean shift tracking algorithm based on this representation is suggested. In addition, a parameter to resize the window around the object is considered, adaptively. Experimental results on some benchmark video in comparison with other state-of-art methods show the efficiency and utility of the proposed algorithm in many complex conditions.

Robust Target Model Update for Mean-shift Tracking with Background Weighted Histogram

This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

A multi-target trapping and tracking algorithm for Bactrocera Dorsalis based on cost model

Researching a robust, high precision, stable performance and applicable target tracking method is of important theoretical significance and practical value. Using computer vision technology to detect and track moving object in video image is a kind of advanced target counting method and it is a research hotspot. That is a complex estimation problem and still faces enormous challenges. Bactrocera dorsalis, which is widely distributed in the southern area of China, has become the most serious pest and need to be controlled frequently. In this study, a B. dorsalis tracking and counting system based on computer vision has been built to find out the activity patterns of B. dorsalis so as to provide a better prevention and control of the pest. For counting the number of B. dorsalis passed through the monitoring area in real time, this study has analyzed the advantages and disadvantages of the mainstream methods, such as inter-frame difference method, background-difference method, optical flow tracking method, corner detection method, Mean Shift tracking method and other classical target tracking algorithms. Based on these analyses, this study has proposed a cost model for tracking B. dorsalis, which can track all the trajectories of B. dorsalis in the monitoring area in real time. A multi-B. dorsalis tracking algorithm based on this cost model and a B. dorsalis counting algorithm have been realized. Finally, the result of the experiment shows that the algorithm can solve the overlap of B. dorsalis. Additionally, a field experiment of the tracking algorithm has been carried out for 5days in Guangzhou Yangtao Park. The number of B. dorsalis detected by computer system was 1570 and the manually counted number was 1591.The tracking accuracy is 98.7%.

Object tracking with dual field-of-view switching in aerial videos

Visual object tracking plays an important role in intelligent aerial surveillance by unmanned aerial vehicles (UAV). In ordinary applications, aerial videos are captured by cameras with a fixed-focus lens or a zoom lens, for which the field-of-view (FOV) of the camera is fixed or smoothly changed. In this paper, a special application of the visual tracking in aerial videos captured by the dual FOV camera is introduced, which is different from ordinary applications since the camera quickly switches its FOV during the capturing. Firstly, the tracking process with the dual FOV camera is analyzed, and a conclusion is made that the critical part for the whole process depends on the accurate tracking of the target at the moment of FOV switching. Then, a cascade mean shift tracker is proposed to deal with the target tracking under FOV switching. The tracker utilizes kernels with multiple bandwidths to execute mean shift locating, which is able to deal with the abrupt motion of the target caused by FOV switching. The target is represented by the background weighted histogram to make it well distinguished from the background, and a modification is made to the weight value in the mean shift process to accelerate the convergence of the tracker. Experimental results show that our tracker presents a good performance on both accuracy and efficiency for the tracking. To the best of our knowledge, this paper is the first attempt to apply a visual object tracking method to the situation where the FOV of the camera switches in aerial videos.

Independent Object Tracking from Video using the Contour Information in HSV Color Space

Background/Objectives: In this paper, a tracking method that is based on the colors and contours is proposed to obtain results that are superior to those of the traditional methods for the tracking of the objects in video. Methods/Statistical Analysis: The proposed method requires an initial conversion of the existing RGB-color-based video in the HSV color space, and a back-projection histogram in the HSV color space is used to obtain the color information of the object region that is to be tracked. The contour information is also obtained to track the target based on the Canny algorithm, whereby the moving object in the video is tracked in real time on the basis of the color and contour information. Findings: An experiment was performed wherein the proposed method was compared with the Mean-shift tracking method, the Camshaft tracking method, the Kalman-filter tracking method, and the Particle-filter tracking method in terms of the objective of this paper. The comparison showed that the proposed method is more accurate regarding the tracking of a single object in video. Improvements/Applications: This improved tracking method will be utilized in object tracking, CCTV for car tracking and HCI.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.

Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.

A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking

Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature saliency concept into the background modelling, and put forward a novel foreground saliency-based background-weighted histogram scheme (FSBWH) for target representation and tracking, which exploits salient features from both foreground and background. We think that background and foreground salient features are both crucial for target representation and tracking. Experimental results show that the proposed FSBWH scheme can improve the robustness and performance of tracker significantly especially in complex occlusions and similar background scenes.

Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy.

Red blood cell (RBC) phase images that are numerically reconstructed by digital holographic microscopy (DHM) can describe the cell structure and dynamics information beneficial for a quantitative analysis of RBCs. However, RBCs investigated with time-lapse DHM undergo temporal displacements when their membranes are loosely attached to the substrate during sedimentation on a glass surface or due to the microscope drift. Therefore, we need to develop a tracking algorithm to localize the same RBC among RBC image sequences and dynamically monitor its biophysical cell parameters; this information is helpful for studies on RBC-related diseases and drug tests. Here, we propose a method, which is a combination of the mean-shift algorithm and Kalman filter, to track a single RBC and demonstrate that the optical path length of the single RBC can be continually extracted from the tracked RBC. The Kalman filter is utilized to predict the target RBC position in the next frame. Then, the mean-shift algorithm starts execution from the predicted location, and a robust kernel, which is adaptive to changes in the RBC scale, shape, and direction, is designed to improve the accuracy of the tracking. Finally, the tracked RBC is segmented and parameters such as the RBC location are extracted to update the Kalman filter and the kernel function for mean-shift tracking; the characteristics of the target RBC are dynamically observed. Experimental results show the feasibility of the proposed algorithm.

Robust single object tracker based on kernelled patch of a fixed RGB camera

One of the weaknesses of the mean-shift tracker is its limited ability to track a fast-moving object. Not only does the captured image look blurred but the object will often be out of the search window. Moreover, blur issue is a normal occurrence for a fast-moving object by using a commercially available camera due to a low frame rate. This paper aims to develop a robust observation detector for a single object tracker, especially for blurred image, fast-moving and non-rigid object. We propose a fusion of a kernel-based histogram and feature detector based approach. The measurement input is selected from candidate patches that will be generated by matching the vector descriptor. These vectors are built based on the detected points of interest. Two colour spaces are considered where all three channels of RGB are used, while only the hue channel of the HSV space is utilized. The kernel method is employed for better histogram accumulation. The output patch of the previous frame will be the target model where histogram similarity is measured based on Gaussian distribution. The selected output of the patch matching will undergo position smoothing for better precision. Maximum likelihood approach is used to iterate the patch position until the best match is found. The results indicate that our algorithm has a good detection for challenging surroundings and environments. In some cases, our algorithm may have less accuracy but in no case did it failed to detect.

색상과 채도의 적응적 조합을 이용한 개선된 Mean-Shift 추적

Mean-Shift tracking using hue has a problem that it fail in the object tracking when background has similar hue to the object. This paper proposes an improved Mean-Shift tracking algorithm using new data instead of a hue. The new data is generated by adaptive mixture of hue and saturation which have low interrelationship . That is, the proposed algorithm selects a main attribute of color that is able to distinguish the object and background well and a secondary one which don’t, and places their upper 4 bits on upper 4 bits and lower 4 bits on the mixture data, respectively. The proposed algorithm properly tracks the object, keeping tracking error maximum 2.0~4.2 pixel and average 0.49~1.82 pixel, by selecting the saturation as the main attribute of color under tracking environment that background has similar hue to the object.

Video-based measurement for transverse vibrations of moving catenaries in mine hoist using mean shift tracking

This work proposed a non-contact video-based measurement for transverse displacements of hoisting catenaries in mine hoist using mean shift tracking. Hoisting catenaries are typically moving cables making it impossible to use traditional methods. To obtain the transverse displacements of an arbitrary point in the hoisting catenary, a guide line that was perpendicular to the hoisting catenaries was inserted in the sequential images. Through tracking the dynamic intersection area by mean shift algorithm and then recording the location of the tracker, the transverse vibration displacements of the selected point in the hoisting catenary can be easily obtained. A theoretical model of transverse vibration of a catenary was established, and model validation was then conducted confirming the reasonability and applicability of the proposed method. Eventually, a laboratory experiment was also conducted and then confirmed the accuracy of the proposed method. This work is perfectly suitable for the measurement of moving cable vibration.

Multi-mode Kernel Weight-based Object Tracking

As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.