Method for fast robustness tracking of infrared target based on sparse representation

Abstract

The invention discloses a method for fast robustness tracking of an infrared target based on sparse representation. Sparse solution is carried out by calculating L1 norm minimization, the reconstruction error is calculated to acquire the probability of each particle, and therefore target tracking is achieved. In view of the facts that in the method, because the number of dimensions of a dictionary is too large, considerably much time is consumed in the L1 minimization calculation process, real-time performance of an algorithm can not be met, and gray features can be easily disturbed by noise and illumination changes and similar objects, on the basis of compressive sensing, Harr-like characteristics are compressed based on a random measurement matrix, original image characteristics are well kept, and better expression ability is achieved. Because dictionary construction is carried out through characteristics based on compressive sensing, calculated amount is greatly reduced, the target can be more effectively expressed, problems like illumination changes, shielding, size changes and target deformation are solved, and fast robustness tracking of the infrared target can be achieved.

Description

technical field [0001] The invention relates to a fast and robust infrared target tracking method based on sparse representation. Background technique [0002] Infrared target tracking is the key technology in infrared early warning system, tracking system and precision guidance system. Due to the small imaging area of ​​the target at a long distance, low contrast, blurred edges, and variable size and shape, the detectable signal is relatively weak. The signal-to-noise ratio is low, making it difficult to track infrared targets in complex backgrounds. [0003] Target tracking algorithms for infrared image sequences can be roughly divided into two categories: deterministic methods and random methods. Deterministic methods, such as the Mean Shift algorithm, realize tracking by finding the optimal match of the target. This method has the advantages of good real-time performance and rapid target matching, but it is easy to fall into local extremum under occlusion and other si...

AI Technical Summary

Problems solved by technology

[0006] In order to avoid the shortcomings of the existing technology, the present invention proposes a fast and robust infrared target tracking method based on sparse representation, aiming at the low contrast between the target and the background in the infrared image sequence, the gray features are susceptible to noise, illumination changes and Problems such as interference effects of similar objects

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