Online multi-instance learning-based soccer video player tracking method

Abstract

The present invention discloses an online multi-instance learning-based soccer video player tracking method, and belongs to the computer visual recognition field. The technical scheme is characterized by at the target feature extraction aspect, combining the global features and the local features, extracting a place dominant color and a player template dominant color histogram; at the same time, carrying out the particle initialization on a particle filter motion model, transferring the states of all particles at the previous frame of target player positions, calculating the similarity of all particles and the player template dominant color histogram after the state transfer, removing the influence of the place dominant color, carrying out the normalization on the particle weights according to the similarity value, and using the particles of large weights to substitute to generate a new particle set; obtaining the Haar-like feature vectors of a set image, inputting in a multi-instance learning classifier, and calculating the current frame of target player positions. According to the technical scheme of the present invention, the nondeterminacy of the target motion can be reduced, a drift phenomenon in the tracking is inhibited effectively, and the accuracy of a tracking result is improved.

Description

technical field [0001] The invention belongs to the field of computer vision recognition, and more particularly relates to a football video player tracking method based on online multi-instance learning. Background technique [0002] At present, with the rapid development and application of image processing and machine learning theory, moving object tracking technology has become a research hotspot in the direction of computer vision in recent years. The so-called object tracking refers to the target modeling of the region of interest in the input initial frame. The process of continuously tracking the target in subsequent frames has been widely used in many fields such as video surveillance, military aviation, and intelligent transportation. [0003] Football has become one of the most popular sports in the world, with rich competitions, high popularity, a very large audience group and a high degree of attention to the game. From the perspective of ordinary spectators, the...

AI Technical Summary

Problems solved by technology

[0008] For the above defects or improvement needs of the prior art, the present invention provides a football video player tracking method based on online multi-instance learning, which aims at combining the advantages of global features and local features, and improves the traditional online multi-instance learning tracking method Algorithm, using the motion model of particle filter motion estimation to generate a position candidate set, thus solving the problems of insufficient adaptability of existing tracking technology, prone to tracking drift and unclear identification of player features

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