Mean Shift Tracking Algorithm Research Articles

Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process.

Novel object detection technique for videos with homogeneous background

Object detection and tracking in video frames play a major role in all the computer vision systems. Several computer vision systems use background subtraction method for detecting the objects. However, Background subtraction method often results in lack of accuracy during frame comparison. Also, it leads to increase in memory requirements and computational complexity while setting threshold. The new Local Mean Diamond Pattern (LMDP) is introduced here to overcome the existing issues by extracting the objects directly from the frames. The extracted objects are tracked using mean shift tracking algorithm. The experimental result shows that the LMDP outperforms the existing methods viz., Local Binary Pattern (LBP), Local Extrema Pattern (LEP) and Local Rhombus Pattern (LRP) in terms of accuracy.

Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.

Curling stone tracking based on an enhanced mean-shift algorithm using optimal feature vector

Computer vision technology can automatically detect and recognize objects on the ground or on a court, such as balls, players, and lines, using camera sensors. These are non-contact sensors, which do not interfere with an athlete’s movement. The game elements detected by such measuring equipment can be used for game analysis, judgment, context recognition, and visualization. This paper proposes a method to automatically track the position of stones in curling sport images using computer vision technology. The authors extract the optimal feature vector of the mean-shift tracking algorithm by obtaining the optimal histogram from the color and edge information of the curling stone, thereby adaptively controlling the number of bins in the histogram. After evaluating the performance of the curling stone tracking method among 1424 image frames from curling sport videos, the authors found that the proposed method improved detection rate (overlap threshold = 0.9) by 14.85% compared to the general mean-shift method.

Research and Implementation of Object Tracking

Aiming at the problems that the traditional MeanShift has a singleness feature, this paper introduces depth feature based on binocular vision, thus proposes a depth feature based real-time MeanShift tracking algorithm. Firstly, a similarity measurement method based on advanced quadratic-form distance is put forward. Secondly, the self-adaptive adjustment mechanism of feature weight coefficient and updating strategy of object model are improved based on the color and depth features. Finally, the algorithm is simulated by Matlab. Results show that the proposed algorithm performs well in tracking accuracy, robustness.

Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

Mean Shift Tracking Based on Fuzzy Background Weighting

Aiming at the problem that Mean Shift tracking algorithm cannot track well in complicated background, a Mean Shift algorithm based on fuzzy background weighting is proposed. It introduces a fuzzy membership function based on difference, which makes use of the difference between target model and background model in order to represents each pixel contribution to target exact description, and improves target description accuracy. At the same time, the original scale increment and decrement method is improved by using background information for adapting to target scale changing. Experimental results show that the proposed algorithm solves the problem of small-scale wandering and tracking hysteresis of the scale increment and decrement method to a certain extent, and improves the robustness of Mean Shift algorithm under complex background disturbances.

Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm

In this paper, a new robust mean shift tracker is proposed by utilizing the joint color and texture histogram. The contribution of our work is to take local phase quantization (LPQ) operator advantage of texture features representation, and to combine it with a color histogram mean shift tracking algorithm. The LPQ technique can be applied to obtain the texture features which represent the object. In texture classification, The LPQ operator is much robust to blur than the well-known local binary pattern operator (LBP). Compared with traditional color histogram mean shift algorithm which considers only color statistical information of the object, the joint color-LPQ texture histogram is more robust and overcome some difficulties of the traditional color histogram mean shift algorithm. Comparative experimental results on numerous challenging image sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods, especially traditional mean shift tracker. The algorithm is evaluated by numerical parameters: the center location and the average overlap, it proved the tracking robustness in presence of similar target appearance and background, motion blurring.

Meanshift tracking algorithm based on SLIC superpixel

Meanshift tracking algorithm based on SLIC superpixel

Tracking Algorithm with Adaptive Bandwidth of Kernel Function for UAV Autonomous Aerial Refueling

The use of machine vision to track the receiver aircraft’s receptacle is one of the key steps in automated aerial refueling. Traditional Meanshift tracking algorithm’s kernel bandwidth is fixed, when the target size changes, it will produce with the bias even with the loss of the situation. Aiming at this problem, this paper proposes a kernel bandwidth adaptive algorithm based on corner matching. First, use backward tracking method to amend the core window center. Then according to the Harris algorithm to detect the corner, use small-scale approach for the two points between frames to match and regression analysis, namely determine the update parameters of nuclear window width. Experiments show that the algorithm can track the target accurately when the size of tracking target change, and the kernel window width can be better suited to the target size.

Extended social force model‐based mean shift for pedestrian tracking under obstacle avoidance

It has been shown that mean shift tracking algorithm can achieve excellent results in pedestrian tracking task. It empirically estimates the target position of current frame by locating the maximum of a density function from the local neighborhood of the target position of previous frame. However, this method only considers its past trajectory without considering the influence of pedestrian environment when applying to pedestrian tracking. In practical, pedestrians always keep a safe distance away from obstacles when programming their paths. To address the issue of obstacle avoidance, this paper proposes a novel extended social force model‐based mean shift tracking algorithm in which pedestrian environment is full taken in consideration. Firstly, an extended social force model is presented to quantify the interaction between pedestrian and obstacle by means of force. Furthermore, directional weights and speed weights are introduced to adjust the strength of the force in terms of the difference of individual perspectives and relative velocities. Finally, the initial target position is predicted by Newton's laws of motion and then the Mean Shift method is integrated to track target position. Experiment results show that this algorithm achieves an encouraging performance when obstacles exist.

Improved FPGA Implementation of Real Time Modified Mean Shift Tracking Algorithm

Objectives: To modify and implement color based mean shift object detection and tracking algorithm utilizing both the parallel and sequential capabilities of Xilinx ZYNQ ZC-702 SoC in order to speed up tracking. Method/ Statistical Analysis: The parallel and sequential processing capabilities of Field Programmable Logic Array (FPGA) and Processing System (PS) respectively are utilized in order to have a standalone system that can be faster, reliable and efficient while tracking the object in real time. Operations such as reading and writing video, grabbing kernel from frame and mean shift vector computation are sequential in nature and are best suited for processing system where as the operations parallel in nature are best fit for FPGA and may include estimation of histogram, computation of weights and estimation of weighted histogram. Executing them in parallel helps in reducing the machine cycles and enhances the fps. Findings: The high computational power of the algorithm is met by collective use of hardware and software while keeping the resources available on FPGA in check. The modified mean shift tracking method helps in exploiting the parallel computation capability of the FPGA. The paper compares the results with various techniques implemented on different embedded boards and the frame processing rate is much better with proposed FPGA implementation of modified mean shift tracking algorithm. Further, the window size can be varied without affecting fps. Application/Improvements: The frame rate achieved by using both hardware and software simultaneously is considerably higher than achieved with earlier implementations.

Midlevel cues mean shift visual tracking algorithm based on target-background saliency confidence map

Because of the well-known merits of non-parametric estimation and fast mode matching, the mean shift tracking algorithm has been proposed with demonstrated success. But the traditional mean shift utilizes center-weighted color histogram as the reference model which is susceptible to interference of background pixels, and that can result in the compromised tracking robustness. In order to solve this problem, we propose a midlevel cues mean shift visual tracking algorithm based on target-background confidence map saliency-weighted model. A discriminative appearance model based on superpixels is introduced, thereby it can facilitate a tracker to distinguish between target and background by different weights. This improved mean shift tracker is formulated by computing a target-background saliency confidence map and mean shift iteration, and then we can obtain the position in next frame. Experimental results demonstrate that the improved mean shift tracker is able to handle occlusion and recover it from tracking drifts, furthermore, the improved algorithm facilitates foreground object segmentation during tracking.

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.

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.

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.

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.

The Improvement of Mean-Shift Algorithm in Target Tracking

The bandwidth of kernel function is invariance in Mean-Shift tracking process which led to the problem of actual process of tracking failure. To solve this question a method is proposed to combine object contour, RGB color histogram and Mean-Shift tracking algorithm. In each frame the contour and color feature of target object are extracted to as a model. Meanwhile the size of contour is used as the bandwidth for kernel function in the next frame. Based on this Method the accuracy of Mean-Shift tracking is improved and the error of Mean-Shift tracking is reduced.

Multi‐scale mean shift tracking

In this study, a three‐dimensional mean shift tracking algorithm, which combines the multi‐scale model and background weighted spatial histogram, is proposed to address the problem of scale estimation under the framework of mean shift tracking. The target template is modelled with multi‐scale model and described with three‐dimensional spatial histogram. The tracking algorithm is implemented by three‐dimensional mean shift iteration, which translates the problem of scale estimation in two‐dimensional image plane into the localisation in three‐dimensional image space. To enhance the robustness, the background weighted histogram is employed to suppress the background information in the target candidate model. Firstly, the multi‐scale model and three‐dimensional spatial histogram are introduced to represent the target template. Then, the three‐dimensional mean shift iteration formulation is derived based on the similarity measure between the target model and the target candidate model. Finally, a multi‐scale mean shift tracking algorithm combining multi‐scale model and background weighted spatial histogram is proposed. The proposed algorithm is evaluated on some challenging sequences which contain scale changed targets and other complex appearance variations in comparison with three representative mean shift based tracking algorithms. Both the qualitative results and quantitative analysis indicate that the proposed algorithm outperforms the referenced algorithms in both tracking precision and scale estimation.