A football video player tracking method based on online multi-instance learning

Abstract

The invention discloses a football video player tracking method based on online multi-instance learning, which belongs to the field of computer vision recognition. In terms of target feature extraction, this technical solution combines global features and local features to extract the histogram of the main color of the field and the main color of the player template; at the same time, the particle initialization of the particle filter motion model is performed, and all the particles of the target player position in the previous frame are processed. State transition, calculate the similarity between all particles after the state transition and the main color histogram of the player template, remove the influence of the main color of the field, normalize the particle weights according to the similarity value, and replace them with particles with large weights to generate new The particle set; get the Haar‑like feature vector of the set image, input it into the multi-instance learning classifier, and calculate the target player position in the current frame. The technical scheme of the invention can reduce the uncertainty of target motion, effectively suppress the drift phenomenon in tracking, and improve the accuracy of tracking results.

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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