Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking

When the background is complex and there is a lot color similar pixel interference, it may lead to location and size of Camshift algorithm’s search window abnormal so as to tracking failure. Aiming at these problems, this paper proposed a algorithm that combinating Camshift algorithm and Kalman filter. Kalman filter can predict the position of the moving object. Camshift algorithm adjusted the position and size of search window by using the prediction, so as to ensure the correct operation of the Camshift algorithm. Experimental results show that the proposed algorithm can effectively overcome the large area of similar color background and occlusion and many colors similar moving targets interference and other issues, improve the accuracy and robustness of target tracking algorithm.

Download Full-text

Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.3672 ◽

2013 ◽

Vol 380-384 ◽

pp. 3672-3677 ◽

Cited By ~ 2

Author(s):

Bao Hong Yuan ◽

De Xiang Zhang ◽

Kui Fu ◽

Ling Jun Zhang

Keyword(s):

Kalman Filter ◽

Object Tracking ◽

Moving Objects ◽

Video Tracking ◽

Moving Object ◽

Large Area ◽

Image Region ◽

Future State ◽

Kalman Filter Algorithm ◽

Histogram Similarity

In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

Download Full-text

Adaptive Cubature Kalman Filter Algorithm Based on Quaternion Error Modeling

2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) ◽

10.23919/icins43215.2020.9133740 ◽

2020 ◽

Author(s):

Kai Liu ◽

You Zhao ◽

Zhi-Gang Zhu

Keyword(s):

Kalman Filter ◽

Error Modeling ◽

Cubature Kalman Filter ◽

Kalman Filter Algorithm

Download Full-text

Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽

10.3390/en14040924 ◽

2021 ◽

Vol 14 (4) ◽

pp. 924

Author(s):

Zhenzhen Huang ◽

Qiang Niu ◽

Ilsun You ◽

Giovanni Pau

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Kalman Filter ◽

Human Body ◽

Wearable Devices ◽

Human Motion ◽

Data Set ◽

Feature Extraction Method ◽

Motion Data ◽

Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

Download Full-text