A target tracking method and device

Abstract

The embodiment of the present invention discloses a target tracking method and device, which are used to reduce the computational complexity of target tracking and have stronger generalization ability. The method may include: acquiring a first frame image to be processed from video data collected by a camera; performing target detection on the first frame image to generate a target detection result, where the target detection result includes: detecting from the first frame image the first target; adopt the Kalman motion model to perform motion estimation on the first target, generate a target tracking result, and the target tracking result includes: the predicted position of the first target in the next frame image relative to the first frame image; according to The target detection result and the target tracking result estimate the motion state of the first target.

Description

technical field [0001] The present invention relates to the field of computer technology, and in particular, to a target tracking method and device. Background technique [0002] In the visual tracking technology of the target, the motion state of the target can be tracked in real time through a single camera. The target tracking algorithm used in the prior art is mostly divided into two processes. The first step is to independently detect the visual target in each frame of the image. For example, the visual target can be a pedestrian, a vehicle, etc. The commonly used target detection algorithm has deformable components. Models, Regions with Convolutional Neural Network (RCNN), Faster Regions with Convolutional Neural Network (Faster RCNN), etc. The second step is to associate the visual targets detected in consecutive frame images. According to the similarity calculation between the target features, a similarity matrix or loss matrix can be obtained, and the target matchi...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, target detection algorithms all use complex motion models, which lead to high computational complexity, making the processing speed of the target tracking algorithm around 1 hertz (Hz) or even lower, that is, the target tracking algorithm processes frame The rate can only be processed once per second, which is far from meeting the needs of real-time usage scenarios

As another example, algorithms based on deformable component models, RNN, and Faster RCNN in the prior art have weak generalization ability and are easy to overfit to certain types of motion modes, making it difficult to promote them to more stable and general motion modes.

Therefore, the main disadvantages of the target tracking algorithm in the prior art are that the computational complexity is too high and the generalization ability is weak

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