Novel L1 regularization-based real-time moving target tracking method

Abstract

The invention discloses a novel L1 regularization-based real-time moving target tracking method which comprises the following steps of: inputting a first frame of image and determining a target to be tracked; initializing a tracking gesture; initializing a template set; carrying out particle initialization on particle filter; obtaining the next frame of image, turning to the next step for tracking until the last frame of image; preprocessing the image; calculating the similarity of particles and templates; re-sampling the particles with the maximum observation probability; detecting shielding; and updating the templates. Through adding two norms of coefficients of few templates into an L1 minimization model, a new minimization model is established, whether the target is shielded is detected by using a shielding detection method before the templates are updated, and thus the precision of tracking the target is improved; and a new minimization model is solved by using differential coefficient boundary and analyzable presentation, and thus a new algorithm can be suitable for tracking the real-time moving target. According to the invention, the target tracking accuracy can be determined to ensure that the algorithm meets the performance requirement of actual application.

Description

technical field [0001] The invention belongs to the technical field of intelligent video monitoring, and in particular relates to a new method for real-time moving target tracking based on L1 regularization. Background technique [0002] At present, the application of video surveillance technology is very extensive, and video moving target tracking technology has become one of the hot topics of research. It integrates knowledge and technology in many related fields such as computer image processing, pattern recognition, artificial intelligence and automatic control. The research purpose of video target tracking is to simulate the human visual motion perception function, endow the machine with the ability to identify moving targets in sequence images, and provide important data basis for video analysis and understanding. [0003] In recent years, due to the high robustness of sparse representation to image erosion, especially occlusion, sparse representation and compressive s...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the existing interior point method based on the preprocessing conjugate gradient to solve the L1 regularization method: due to the high computational complexity, the tracking speed is slow and cannot meet the real-time requirements, and it cannot detect whether the target is occluded. The invention provides a new method for real-time moving target tracking based on L1 regularization

Images