Target tracking method and device

Abstract

The invention provides a target tracking method and device. The target tracking method and device are applied to a server storing feature models corresponding to target objects. The method comprises the steps of performing target detection on a current video frame image, and extracting corresponding CNN features according to position information, obtained through detection, of all to-be-detected objects; calculating to obtain a corresponding similarity matrix according to the position information and the CNN features of each to-be-detected object and the position information and the feature model of each target object in the previous video frame image; performing data association on each to-be-detected object and each target object based on the similarity matrix to obtain an optimal matching result; and if a to-be-detected object successfully matched with the corresponding target object exists in the optimal matching result, updating the corresponding feature model according to the CNNfeatures of the to-be-detected object, and obtaining a corresponding tracking result based on the to-be-detected object. The method has the advantages of strong anti-interference capability and hightracking success rate, and can continuously track the target object.

Description

technical field [0001] The present invention relates to the technical field of multi-target tracking of video images, in particular to a target tracking method and device. Background technique [0002] With the continuous development of surveillance technology, the application of multi-target tracking technology for tracking multiple target objects in surveillance video is becoming more and more extensive. In the process of tracking the target object, the existing multi-target tracking scheme combines the CNN (Convolutional Neural Network, Convolutional Neural Network) feature of the target object in the current video image with the CNN feature when the target object was successfully tracked recently. The comparison method is used to track the target object, but this multi-target tracking solution has weak anti-interference ability and low target tracking success rate, usually because the CNN feature of the target object recently successfully tracked carries some occlusions ...

AI Technical Summary

Problems solved by technology

In the process of tracking the target object, the existing multi-target tracking scheme combines the CNN (Convolutional Neural Network, Convolutional Neural Network) feature of the target object in the current video image with the CNN feature when the target object was successfully tracked recently. The comparison method is used to track the target object, but this multi-target tracking solution has weak anti-interference ability and low target tracking success rate, usually because the CNN feature of the target object recently successfully tracked carries some occlusions , so that the CNN features of the target object in the current video image cannot be correctly matched with the CNN features of the target object when it was successfully tracked recently, resulting in tracking failure

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