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首页> 外文会议>IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition >Background Categorization for Automatic Animal Detection in Aerial Videos Using Neural Networks
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Background Categorization for Automatic Animal Detection in Aerial Videos Using Neural Networks

机译：基于神经网络的空中视频自动动物检测的背景分类

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摘要

This paper addresses the problem of animal detection in natural environment from aerial videos. Since the natural environment is usually composed of several fundamental elements such as trees, grass, streams, etc., it is proposed to distinguish the animal by categorizing the background into several classes. From the manually labeled samples, texture as well as brightness features are extracted to train a feedforward Neural Network. Then the classifier is applied to filter the test frame to locate potential animal regions. Four texture measures calculated from Grey Level Co-occurrence Matrix (GLCM) are used for texture feature description. Instead of obtaining these texture measures from grey level images, it is proposed to carry out calculation for every channel of the RGB image. The implemented results illustrate that this feature extraction method works well and the texture feature is a decisive factor in background categorizing.
机译：本文涉及从航行视频从天然环境中动物检测问题。由于自然环境通常由诸如树木，草，流等的若干基本元素组成，因此建议通过将背景分类为几个类来区分动物。从手动标记的样本，纹理以及亮度特征被提取以训练前馈神经网络。然后应用分类器以过滤测试帧以定位潜在的动物区域。从灰度共发生矩阵（GLCM）计算的四个纹理测量用于纹理特征描述。提出了从灰度图像从灰度图像获得这些纹理度量，而是针对RGB图像的每个频道进行计算。实施的结果说明了该特征提取方法运作良好，纹理特征是背景分类中的决定性因素。

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