Abstract
The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured.
Highlights
Academic Editor: Zhihan Lv e original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and there are problems such as poor tracking accuracy, target loss, and model mismatch. e interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and combines the updated weight values of each filter to obtain a weighted sum. erefore, the interactive multimodel algorithm can achieve better performance. is paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching
This paper proposes an improved Kalman filtering (IKF) algorithm. is method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation
In order to accurately track players and improve the accuracy of player trajectories, this paper has carried out a research on the matching algorithm of player trajectories based on interactive multimodels. e research mainly includes three aspects: (1) Propose an improved Kalman filtering (IKF) algorithm. is method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation
Summary
Many researchers have conducted a lot of research on the target motion model and have achieved remarkable results in the improvement of the target model. e earliest use of differential polynomials to approximate the target’s motion trajectory, but this target model is difficult to match the real motion characteristics of the target [9]. E key of the multimodel algorithm is to use multiple motion models to track the target, obtain the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and combine the weight values updated by each filter to weighted summation to obtain the final target state output result. En, based on the IMM algorithm, a multistation pure azimuth maneuvering target tracking method based on the least square method is proposed, and the performance of this method is verified to be better than the multistation CMKF filter in terms of convergence speed and tracking accuracy. Traditional matching algorithms generally only calculate all relevant and irrelevant feature points between two images, resulting in a high false matching rate and low efficiency. Chen et al [34] proposed a matching algorithm based on improved ORB and symmetric matching. ese methods are all aimed at reducing mismatches based on the fast computing power of the ORB algorithm
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