Feature Extraction and Target Recognition of Moving Image Sequences
Published: 10 August 2020
in IEEE Access
IEEE Access pp 1-1; doi:10.1109/access.2020.3015261
Abstract: The detection and recognition of moving objects in image sequence images involve many aspects, such as pattern recognition, image processing, and computer vision. The main difficulties of target detection and recognition are complex background interference, local occlusion, real-time recognition, illumination changes, target size type changes, etc. However, it is very difficult to solve these problems in practical applications. This article introduces image pre-processing for the pre-processing of image sequences. Selectively we highlight the visually obvious features that are helpful for target detection in the image, weaken the image background and features that are not related to the target, and improve the quality of the image sequence. A multi-information integrated probability density estimation kernel integrating gray scale, spatial relationship and local standard deviation information is designed, and the multiinformation integrated kernel is used to extract the feature of the moving target. In terms of moving target recognition, Naive Bayes is used as a weak learner. In order to avoid the over-fitting of the classifier caused by high-noise moving image sequence features, the regularized Adaboost recognition model is introduced as a moving target recognition classifier. In order to completely separate the target and the background, we propose a moving target extraction method based on multi-information kernel density estimation, and input relevant target feature description vectors into the regularized Adaboost-based moving target recognition framework. Robust target recognition performance is obtained, and the reliability of target recognition under high noise data is improved.
Keywords: feature extraction / target recognition / estimation / object detection / kernel / image sequences / Gray-scale
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