Target Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

The Development of Image Processing Board Based on Target Tracking

2013 ◽  
Vol 411-414 ◽  
pp. 1322-1325
Author(s):  
Ya Hui Hu ◽  
Le Jiang Guo ◽  
Xiao Lei ◽  
Cheng Min
Keyword(s):  
Image Processing ◽  
Target Tracking ◽  
Internal Structure ◽  
Development Process ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Algorithm Analysis ◽  
Mean Shift Algorithm ◽  
Specific Target ◽  
Target Environment

This paper selects the target tracking algorithm suitable for specific target environment: using Mean Shift algorithm based on space edge direction histogram at initialization, selecting tracking algorithm based on block when there is a shelter. On the basis of algorithm analysis and software experiment and studying of TI Company's TMS320DM642 DSP chip internal structure and development process, these two algorithms researched in this paper were transplanted to DSP platform and a series of optimization were been made to the algorithms codes after transplanted ,implementing target tracking and identified via DSP development board instead of PC.

Target Tracking Analysis Based on Corner Registration

2013 ◽  
Vol 760-762 ◽  
pp. 1997-2001
Author(s):  
Zheng Xi Kang ◽  
Hui Zhao ◽  
Yuan Zhen Dang
Keyword(s):  
Kalman Filter ◽  
Theoretical Analysis ◽  
Target Tracking ◽  
Affine Transformation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Transformation Theory ◽  
Mobile Target ◽  
The Mean ◽  
Initial Iteration

Target tracking algorithm based on Mean-Shift and Kalman filter does well in linear tracking. However, the algorithm might lose the target when the trace of mobile target is curve or the acceleration is not constant. To cope with these drawbacks, this paper proposes Target Tracking Analysis Based on Corner Registration. The algorithm modifies the initial iteration center of Mean-Shift by using the corner features combined with affine transformation theory and then the Mean-Shift can track the target. The theoretical analysis and the experimental results demonstrate that this method can overcome the drawbacks we talk above and make achievements in target tracking.

Research on target tracking algorithm based on mean shift with adaptive bandwidth

2021 ◽  
pp. 1-15
Author(s):  
Ming Han ◽  
Jingqin Wang ◽  
Jingtao Wang ◽  
Junying Meng ◽  
Ying Cheng
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Kernel Function ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Distance Constraint ◽  
Mean Shift Algorithm ◽  
Signed Distance ◽  
Adaptive Bandwidth ◽  
Anisotropic Kernel

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.

An Adaptive Tracking Algorithm Based on Mean Shift

2012 ◽  
Vol 538-541 ◽  
pp. 2607-2613 ◽  
Author(s):  
Zheng Hong Deng ◽  
Ting Ting Li ◽  
Ting Ting Zhang
Keyword(s):  
Kalman Filter ◽  
Mean Shift ◽  
Initial Point ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Statistical Probability ◽  
Mean Shift Algorithm ◽  
Target Model ◽  
The Mean ◽  
Selective Mechanism

Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. Meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm. We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Vol 765-767 ◽  
pp. 720-725 ◽  
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Complex Environments ◽  
The Real ◽  
Real Environment ◽  
Background Clutter ◽  
Weighted Histogram

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

Efficient Multi-Target Tracking Based on Meanshift and PNN

2013 ◽  
Vol 380-384 ◽  
pp. 3946-3949
Author(s):  
Zhi Ming Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Video Analysis ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Background Model ◽  
Probability Neural Network ◽  
Multi Target Tracking

Multi-target tracking is one of the basic and difficult tasks in video analysis and understanding. This paper proposed an efficient tracking algorithm based on meanshift algorithm and PNN (Probability Neural Network) background model. Firstly, PNN detection results were used to initialize targets for meanshift tracking. Secondly, in the succeeding frames, every target was matched to detected regions before tracking. At last, only targets which couldnt match with new regions need tracking with meanshift tracking algorithm. Experimental results show that mean search steps for every target were dramatically reduced compare with original mean shift tracking algorithm.

The Improvement of Mean - Shift Algorithm in the Video of Global Visual Robotic Fish in Tracking Moving Targets

2013 ◽  
Vol 475-476 ◽  
pp. 947-951
Author(s):  
Zhi Yuan Mai ◽  
Kun Yu Tan ◽  
An Ting Xu ◽  
Wei Xiang
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Robotic Fish ◽  
Experimental Results ◽  
Moving Targets ◽  
Mean Shift Algorithm ◽  
Good For ◽  
The Difference

The tracking effect is not good for the faster track with Mean Shift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish.To solve the difficulty of tracking drastically moving targets in this paper, determining the position of moving targets in the next frame through comparing with two bc coefficients which have been set when the Epanechnikov has been selected core to estimate is indeed. The experimental results show the proposed algorithm can track the moving targets efficiently and precisely in video,and also can meet high real-time situation with small calculation.

Sign in / Sign up

Export Citation Format

Share Document