Object tracking method and object tracking system based on local classification

Abstract

The invention provides an object tracking method and an object tracking system based on local classification. The object tracking method comprises the steps of acquiring a positive sample and a negative sample based on a first frame, and dividing into a training sample set and a verification sample set; performing local block sampling on the training sample set and training a local classifier of the object and acquiring an objective model; utilizing a candidate in subsequent frame pictures based on a particle filtering frame, performing estimation on each candidate by means of a local block and a corresponding local classifier, and updating the object position to the position of the candidate with maximal confidence. Therefore the invention provides the object tracking method and the object tracking system which overcome a drift tracking problem in a blocked condition, wherein the drift tracking problem cannot be settled based on global object tracking. Furthermore the invention provides a sample updating mode based on double-threshold restriction. Furthermore the weight of the local classifier is updated based on time domain stability of the object. Partial shielding can be effectively determined and suppressed, and furthermore randomness and contingency are prevented.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to an object tracking method and system based on local classification. Background technique [0002] Target tracking is to estimate the motion state and trajectory of the target of interest in a continuous image or video sequence. It is an important branch of computer vision and is widely used in various fields such as behavior analysis, video surveillance, intelligent transportation, and national defense construction. In recent years, although object tracking has made great progress [1-5] , but due to environmental factors such as illumination changes, scale changes, and local occlusions, the robustness and stability of current tracking algorithms still have certain limitations. [0003] Existing target tracking algorithms can be roughly classified into generative methods and discriminative methods. The generative method refers to finding the most similar region to the ...

AI Technical Summary

Problems solved by technology

[0013] Problems in the existing discriminative tracking methods: 1) Usually, the target is modeled as a whole to train a global classifier, and tracking drift is prone to occur in occluded scenes. 2) When updating samples in traditional sample update methods, The update constraint is single and the update efficiency is low, so that the model cannot adapt well to the change of the target appearance. Therefore, the present invention innovatively proposes a target tracking technology solution based on local classification

Images