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首页> 外文期刊>International journal of advanced pervasive and ubiquitous computing >Detection and Tracking Cows by Computer Vision and Image Classification Methods
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Detection and Tracking Cows by Computer Vision and Image Classification Methods

机译：通过计算机视觉和图像分类方法检测和跟踪奶牛

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In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.
机译：在本文中，研究了视频序列中的牛识别和牵引力。在识别阶段，本文对不同分类算法和特征提取算法的一些讨论和分析进行了一些讨论和分析，并且母牛的检测变为二进制分类问题。检测方法使用多个特征融合的方法提取牛的特征。这些特征包括边缘字符，反映牛身轮廓，灰度值和空间位置关系。此外，该算法通过培训的分类器检测牛体，通过温和的Adaboost算法训练。实验表明，当目标具有变形或目标和背景之间的对比度时，该方法具有良好的检测性能。与通用目标检测算法相比，该方法降低了未命中率，提高了检测精度。检出率可达97.3％。在牵引阶段，提出了流行的压缩跟踪（CT）算法。通过自适应地计算图像块的PAP距离来改变学习率。此外，停止目标模型的更新以避免在分类响应值为负时引入误差和噪声。实验结果表明，当有大覆盖物或态度经常改变姿态时，改进的跟踪算法可以有效地解决目标模型更新。对于牛体的检测和跟踪，构建牛图像的图像和跟踪框架，并且检测器与跟踪框架组合。在复杂环境下的一些视频序列的算法测试表明了基于改进的压缩感知的检测算法在变化和复杂的背景下显示出良好的跟踪效果。

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