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 multi-information 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.
Highlights
The actual scene where the moving target is located is generally a more complicated scene [1,2,3]
The image sequence contains a lot of interference noise, such as shadows cast by the target, noise from the sensor, obstruction by obstacles, and ripples on the water surface [4,5]
The designed kernel density estimation method based on multi-information integrated kernel can distinguish the target from the background sampling area obviously, even if the image target is not located in the center of the processed image, the target can be effectively extracted
Summary
The actual scene where the moving target is located is generally a more complicated scene [1,2,3]. The swaying of the branches in the scene where the moving target is located, the reflection interference of the water surface, and the light changes in the cloudy and sunny days will cause unpredictable changes in the pixels of the image [6]. Supervised recognition can design classifiers by learning labeled data and mining known information, which can obtain a higher accuracy model with a smaller training set [27]. The recognition method of hybrid generation-discrimination learning has received extensive attention [28]. The designed kernel density estimation method based on multi-information integrated kernel can distinguish the target from the background sampling area obviously, even if the image target is not located in the center of the processed image, the target can be effectively extracted.
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