Real-time target tracking method of address event driven unstructured signal

Abstract

The invention discloses a real-time target tracking method of an address event driven unstructured signal. The real-time target tracking method comprises the following specific implementation steps: (1) reading a data packet and preprocessing the data packet; (2) constructing a plane matrix; (3) calculating the membrane voltage at the position corresponding to each address event; (4) judging effective event points; (5) constructing a local space-time plane; (6) estimating a motion vector of the current address event; (7) judging whether the timestamp of the current address event is within thetime interval; (8) correcting the motion vector; And (9) determining the position of a target in the current address event sequence. According to the method, the motion vector of each read-in addressevent is estimated, the motion vector is corrected, the motion information and the space structure of the object are recovered, and the accuracy and the stability of target tracking are improved whilethe calculated amount is reduced and the speed is increased.

Description

Technical field [0001] The present invention belongs to the field of physics technology, and further relates to a real-time target tracking method for address event-driven unstructured signals in the field of visual signal processing technology. The invention can be used in the technical fields of intelligent transportation, human-computer interaction, video monitoring, robot navigation, virtual reality, etc., to detect and track moving targets in three-dimensional space. Background technique [0002] Moving target tracking is one of the key issues in machine vision research. The main task is to estimate the trajectory of the target in the video, that is, to collect a video image sequence from a traditional image sensor, detect the moving target to be tracked, and then determine it in each frame of image The position of the moving target, but traditional image sensors cannot collect fast-moving targets such as missiles and airplanes, and a large amount of redundant data brings hu...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that because the search window used by the Camshift algorithm contains multiple candidate models with similar features, the Camshift algorithm cannot distinguish whether there is same-color interference, resulting in low tracking accuracy

The disadvantage of this method is that the construction of the mapping matrix requires many hyperparameters, which cannot adapt to the target tracking in any actual scene, and the generalization is low.

On the other hand, the Kalman filter continuously corrects the estimated value of the motion state through the input and output observation data of the model, combined with the actual motion parameters, which consumes a lot of memory and cannot be tracked in real time.

Images