Visual tracking method based on multi-cue fusion

Abstract

The invention discloses a visual tracking method based on multi-cue fusion, which belongs to the technical field of information. The method comprises the following steps: a) determining a tracking window comprising a target region and a background region in a first frame of a video sequence; b) obtaining a color feature probability distribution graph, a position feature probability distribution graph and a motion continuity feature probability distribution graph of the previous frame from the second frame; c) adding the three probability distribution graphs in a weighed manner to obtain a total probability distribution graph; and d) using a CAMSHIFT algorithm to obtain the coordinates of a central point of the tracking window of the current frame in the total probability distribution graph. The method can be used in human-computer interaction, visual intelligent surveillance, intelligent robot, virtual reality technology, model-based image encoding, content retrieval of streaming media and other fields.

Description

technical field [0001] The invention relates to visual tracking, in particular to a visual tracking method for fusing multiple clues, and belongs to the field of information technology. Background technique [0002] With the rapid development of information technology and intelligence science, computer vision, which uses computers to realize human visual functions, has become one of the most popular research directions in the computer field. Among them, visual tracking is one of the core problems of computer vision, which is to find the position of the moving target of interest in each frame of the image sequence. It is very necessary and urgent to study it. [0003] Hong Liu et al published the paper "Collaborative mean shift tracking based on multi-cue integration and auxiliary objects" in "Proceedings of the 14th IEEE International Conference on Image Processing (ICIP 2007)" (IEEE 14th International Conference on Image Processing) in 2007 (Collaborative mean shift track...

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Problems solved by technology

However, it assumes that the background model obeys a single Gaussian model. It needs to train the video sequence without moving objects in advance to obtain the background initial model, which limits its application. In the cue evaluation function, a rectangle slightly larger than the target is used to represent the sense The area of ​​interest, the area between the rectangle and the tracking window is defined as the background area. For the reliability evaluation function of a certain clue, the size of the background area directly affects its value, that is, the larger the tracking window, the greater its reliability evaluation function The smaller the value, the lack of generality

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