Abstract
Moving target detection and recognition methods are the foundation and key of modern intelligent video recognition systems. It combines advanced technologies in many fields such as image processing, pattern recognition, and artificial intelligence, and is a research hotspot in computer vision technology. Therefore, it is of great significance to study moving target detection and recognition algorithms. The non-local image extraction algorithm proposed in this paper uses an adaptive clustering method to perform fine cluster analysis on non-local image blocks with different feature types. Through the step-by-step principal component approximation method, we carefully find the features in each class. This progressive principal component approximation implements singular value hard threshold processing based on the Marchenko-Pastur (MP) theorem to select the main part of the feature, and uses special soft thresholds in the principal component transform domain to further improve the extraction performance. The Lower Bound-Based Within-Class Maximum Division (LBWCMD) is proposed, and this method is used as a preprocessing step of robust principal component analysis in moving target detection. This article applies LBWCMD to the video frame set based on the position information of the moving target, and the obtained frame subset meets the signal requirements of Robust Principle Component Analysis (RPCA) to the greatest extent. On this basis, we add frames with smaller motion amplitudes to each frame subset to increase the proportion of background pixels in each subset. Frame set division and low-rank decomposition realize the detection of moving targets under a unified framework. The detection rate of the proposed method is higher than that of the current popular methods in sports video data sets, and the detection accuracy is improved compared with the original RPCA method.
Highlights
As the field of computer vision continues to mature and develop, human society is gradually becoming intelligent and advanced
We display the average of the results of all Ground Truth (GT) corresponding frames of each video in Figure 11(d), and we can find that Adaptive Principal Component Extraction Algorithm (APCEA) has the best effect
In order to overcome the shortcomings of traditional clustering algorithms that it is difficult to adaptively determine the number of classes, we propose an adaptive clustering algorithm suitable for extraction problems to obtain the extraction effect of detail preservation
Summary
As the field of computer vision continues to mature and develop, human society is gradually becoming intelligent and advanced. The main purpose of computer vision research is to realize the use of computers to replace the brain and human eyes to capture object information in the environment, and solve high-level vision problems, and truly realize the description, storage, recognition. In today’s society, with the continuous improvement of computer processing capabilities, engineers hope that computers can replace human eyes and brains to recognize, observe, and interact with external things and the objective world like humans. This requires computers to have human visual processing systems. The main content of computer vision technology research is how to use computer vision technology to solve related human-centered problems, including
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