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A tracking algorithm based on improved Camshift and UKF is proposed in this paper to deal with the problems which exist in traditional Camshift algorithm, such as artificial orientation and tracking failure under color interference as well as object’s changed illumination occlusion. Meanwhile, in order to solve the sheltered problem, the UKF is combined with improved Camshift algorithm to predict the position of the target effectively. Experiment results show that the proposed algorithm can avoid the interference of the background color and solve the sheltered problem of the object, so that achieving a precise and timely tracking of moving objects. Also it has better robustness to color noises and occlusion when the object’s scale changes and deformation occurs.

Target tracking in video sequence is an important research subject in computer vision field; it has been widely applied in video surveillance system, aerospace system, intelligent traffic management, medical diagnosis, military and so on. In recent years, the domestic and foreign scholars have conducted a lot of research on this field and come up with a lot of different tracking methods. Meanshift [

According to the above problems, improved Camshift algorithm combined with Unscented Kalman Filter (UKF) [

Camshift is an improved algorithm based on Meanshift, mainly composed by reverse projection, Meanshift algorithm and Camshift algorithm [

Camshift algorithm does continuous Meanshift operation for all frames of the video sequence, refers to the center position and the size of the search window by calculating as initial value for next frame’s Meanshift search window. Iteration continues like this, target can be tracked.

According to the nonlinear problems in maneuvering tracking, improving the filtering effect, KF filter was presented based on the Unscented transform namely UKF by Julier [

UKF used for target tracking can be divided into two parts, namely, state transform model and state observation model. The state is the state of target, observation is for sequence image; target state includes center position’s coordinates and speed of target. Target’s speed changes randomly, assuming that its acceleration is

where

Assuming

Unscented transform is the core of UKF algorithm, also is an important method for nonlinear state estimation. Assuming state mean and variance of state vector with

vector of sigma points is

mation are as follows:

1) Initialization

where

2) Calculate sigma points using Equation (7)

where

3) Time updating. Take sigma points into state transition Equation (8) and observation Equation (9), calculate the average value of state vector at time

where

4) Observation update equation. Take Equation (8) and Equation (9) into Equation (11) and Equation (12), calculate gain by Equation (13)

Take gain into Equation (14) and Equation (15), update mean and variance of state vector.

Predict position of fast moving target by UKF filter, due to uncertainty of moving object and moving model, feedback Camshift’s tracking result to UKF for updating and correcting its state model each time.

SURF feature can better describe the target texture and spatial information, its performance is roughly the same as SIFT. Due to the adoption of box shaped filtering and integral image, its computing speed is nearly four times faster than SIFT, also it has good real-time performance. Process of SURF feature extraction is shown in

Process of SURF feature extraction: firstly, establish integral image for each frame image, increase window size of box shaped filter gradually, do fast convolution for integral image, thus construct the Pyramid scale space; then subtract two adjacent images on each layer in Pyramid to obtain the differential scale space, compare each point of this space with all the points on the adjacent scale and the same scale of 3 * 3 * 3 Stereo neighborhood, obtain extreme points by the Hessian matrix, next do interpolation calculation and optimization to get the stable feature points; find the main direction around the feature points, construct the region, do Hart wavelet transform for points on the region, extract SURF description vector; at last, calculate Euclidean distance of the two images’ feature vector respectively and match with method of nearest neighbor matching.

In this paper, firstly target tracking template is acquired from video frames, then extract SURF feature points of

target template and initialize position and size of the target. Assuming target’s Center of last frame is

where

New target’s center

where

The traditional Camshift algorithm transforms color space from RGB to HSV, regards the H component in HSV as the histogram’s template information to get distribution of color probability, in order to reduce the influence of illumination on target tracking, but when the background color is similar to target’s, accuracy of target tracking will greatly decreases. Because of SURF’s rotation and scale invariance, also it adapts to illumination change easily, in some occlusions or chaotic scenes, it can still maintain invariant advantages. An improved target tracking algorithm proposed in this paper with the fusion of Camshift and SURF. The block diagram of the improved Camshift algorithm is shown in

Implementation steps of the improved Camshift algorithm:

1) Initialize the first frame to determine the target template, set size and position of the tracking window, extract SURF feature and histogram of target template;

2) Find the centroid in the search window by Camshift algorithm, obtain new size

3) At the same time, use tracking algorithm of SURF’s feature matching to find center position

4) After the two above tracking, use

In the next frame of video image, initialize search window’s position and size by value of the fourth step. Jump to Step 2 and continue to run.

Combine the improved Camshift algorithm with UKF filter to avoid the influence of the occlusion, nonlinear motion, fast speed and other factors on tracking. Algorithm’s block diagram is shown in

First of all, initialize the initial state

The algorithm in this paper is implemented based on MATLAB2013a software platform. Experimental video sequence is AVI format, video acquisition speed is 20

the algorithm are verified from color interference and occlusion compared with the traditional Camshift algorithm’s. The experimental results are showed in Figures 4-7.

The experimental results show that, target’s tracking effect of improved Camshift combined with UKF is more obvious than the traditional Camshift algorithm’s under color interference of the similar background occlusion, it can realize the accurate and real-time tracking of moving target. Tracking effects of algorithm in this paper are better than improved Camshift algorithm’s, it can track moving target accurately and real-timely under occlusion by moving targets.

The algorithm in this paper firstly tracks moving target by Camshift algorithm, then linearly weights Camshift’s tracking result with tracking result of SURF to reduce the influence of similar background interference on tracking result, and finally combines the improved Camshift algorithm with UKF to solve the occlusion problem

Algorithm | x Average Error/Pixcel | y Average Error/Pixcel | Average Tracking Error ms/Frame |
---|---|---|---|

Traditional Camshift | 27.6 ± 6.3 | 25.5 ± 7.5 | 6.5 |

Improved Camshift | 14.4 ± 5.2 | 12.5 ± 5.5 | 3.3 |

Algorithm | Average Iteration/Count | Average Location/ms | Average Error/Pixcel |
---|---|---|---|

Improved Camshift | 2.5 | 7.8 | 13.4 |

Algorithm in this paper | 1.6 | 5.7 | 7.1 |

because UKF can predict target even if the target is sheltered or moving nonlinearly. Since the UKF algorithm has a very accurate tracking result even target moves with uncertain direction and strong random, it may be used for multiple targets tracking in the future. Also SURF can’t accurately track the target under unitary texture feature, so in multiple targets tracking we should pay attention to research of tracking characteristics.

The author would like to thank the student, Lv Haidong, who has helped in implementing the proposed algorithm and making this work possible.