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.

Object Tracking by Corrected Background-Weighted Histogram Mean Shift with Sum of Gradient Mode

2013 ◽  
Vol 347-350 ◽  
pp. 3774-3779
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Li Juan Wang ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Iterative Procedure ◽  
Mean Shift ◽  
Target Localization ◽  
Experimental Results ◽  
Complex Environments ◽  
Algorithm Performance ◽  
Background Clutter ◽  
Weighted Histogram ◽  
Gradient Mode

With a focus on complex environments, the present paper describes a new algorithm in scale changed object tracking through color feature. Mean shift (MS) iterative procedure is the best color-based algorithm to find the location of an object. The algorithm performance is not acceptable once tracking scale changed objects in complex environments. In this paper, the main aim is to improve the MS method, using corrected background-weighted histogram (CBWH) algorithm to reduce the interference of background in target localization. To fit the object scale change, the sum of gradient mode (SGM) is employed. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the tracked objects scale changes; 3) it is less prone to the background clutter.

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.

Robust Spatial-Color Feature with New Similarity Measure and Adaptive Template Update for Mean-Shift Tracking

2013 ◽  
Vol 321-324 ◽  
pp. 1021-1029
Author(s):  
Lu Rong Shen ◽  
Xia Bin Dong ◽  
Rui Tao Lu ◽  
Yong Bin Zheng ◽  
Xin Sheng Huang
Keyword(s):  
Object Tracking ◽  
Similarity Measure ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Tracking Accuracy ◽  
Color Feature ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Update Strategy

In this paper, we analyze the object tracking task of mean-shift algorithm. A spatial-color and similarity based mean-shift tracking algorithm is proposed. The spatial-color feature is used to replace the color histogram, and an enhanced algorithm is derived by adopting a new similarity measure. We also introduce Lucas-Kanade algorithm to design a template update strategy, propose a template update algorithm for mean-shift. Experimental results show that these two improved mean-shift tracking algorithms have high tracking accuracy and good robustness to the change of appearance of the object.

Dynamic Determination of Histogram Resolution for Object Tracking using the Mean-Shift Tracking Algorithm

2011 ◽  
Author(s):  
Takayuki Nishimori ◽  
Toyohiro Hayashi ◽  
Shuichi Enokida ◽  
Toshiaki Ejima
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
The Mean

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Moving Target ◽  
Tracking Method ◽  
The Real ◽  
Mean Shift Algorithm ◽  
Compensation Function ◽  
Tracking Technology

In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.

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