Target tracking method based on multi-feature self-adaption fusion and on-line study

Abstract

The invention discloses a target tracking method based on multi-feature self-adaption fusion and on-line study. Target features are extracted and used as template features; three types of features are extracted from each of novel candidate target regions; a self-adaption fusion process is carried out according to distinctiveness and correlation of all the features; Bhattacharyya distances between the features obtained after fusion and the template features are calculated; the Bhattacharyya distances are subjected to uniformization and then used as weights of the novel candidate target regions; the novel candidate target region with the maximum weight, and a target region are subjected to an overlap judgment, if the overlap rate is less than an overlap rate threshold value, a multiple-time region of the novel candidate target region with the maximum weight is input to a detector, and when a recognizer outputs 'yes', the fact that target tracking succeeds is shown, and the recognizer, the template features and the target region are updated; if the recognizer outputs 'no', the fact that a novel target is found is shown; and if the overlap rate is greater than or equal to the overlap rate threshold value, the recognizer, the template features and the target region are updated. Through the target tracking method based on the multi-feature self-adaption fusion and the on-line study, the adaption ability of the target tracking under different scenarios and certain deformation conditions is enhanced, and the problem that tracking drifting is prone to occurrence after occlusion is avoided.

Description

technical field [0001] The invention relates to the field of target tracking, in particular to a target tracking method based on multi-feature adaptive fusion and online learning. Background technique [0002] Video object tracking refers to the process of object detection, representation and trajectory extraction in video sequences. Video object tracking has practical application requirements in video surveillance, event analysis, human-computer interaction and other fields. [0003] At present, the world's most advanced surveillance systems cannot perfectly handle dynamic tracking tasks in complex scenes, such as: deformation, occlusion, lighting changes, shadows or tracking in crowded environments. Object tracking remains a challenge, especially when objects are partially occluded and deformed. [0004] Single-feature-based tracking methods usually initialize the target region, extract arbitrary target features, such as: color features, and search and match them in the ...

AI Technical Summary

Problems solved by technology

[0007] The target tracking method based on multi-features cannot adapt to the change of target shape, and cannot solve the problem of tracking drift after partial occlusion between targets.

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