Image and laser point cloud fused target tracking method, computing device and medium

Abstract

The invention discloses a target tracking method, a computing device and a computer readable medium for tracking a target around a vehicle based on 2D image data and 3D laser point cloud data. The target tracking method comprises the steps of obtaining an optimal estimation of the tracked target at a moment K in a 3D space; obtaining a predicted value of the K + 1 moment in the 3D space by utilizing the optimal estimation of the K moment; judging whether the observation value of the K + 1 moment can be detected from the 3D laser point cloud data of the K + 1 moment or not; if not, projecting the tracking target into a 2D image space by using the optimal estimation of the K moment, and calculating K-time image features of a corresponding target projection area in the 2D image space; and determining a pseudo observation value at the K + 1 moment by utilizing the optimal estimation at the K moment, the predicted value at the K + 1 moment and the image characteristics at the K moment. Theproblem that a tracking object is lost due to the fact that laser point clouds at medium and long distances are sparse and disappear in the tracking process is solved, the object can be accurately tracked in real time, and the method has an outstanding effect on accurately tracking the object in the field of automatic driving.

Description

technical field [0001] The present invention generally relates to the technical field of autonomous driving environment perception, in particular to an object tracking method for fusion of images and laser point clouds. Background technique [0002] Object tracking technology is an important research content in the fields of automatic driving, video surveillance, and human-computer interaction. In the field of autonomous driving, object tracking is to continuously obtain information about various objects (vehicles, pedestrians, bicycles, motorcycles, animals, etc.) around it, and correspond to the same object between different frames. Due to the complex and changeable driving environment, the tracking process usually requires the fusion of data from multiple sensors. Using lidar and cameras is currently a more practical choice. However, for lidar, as the distance increases, the laser points reflected by the object will become very sparse, and there is even no laser point cl...

AI Technical Summary

Problems solved by technology

For 2D space, the image captured by the camera has dense RGB information, but the tracking object in 2D space lacks depth information, so the position information of 3D space cannot be directly generated

In addition, the deep learning method can be used to estimate the depth of the image, but the accuracy of the depth estimation is low, consumes a lot of computing performance, and cannot meet the real-time requirements of automatic driving

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