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Human Detection in Thermal Images Using Low-level Features

机译：使用低级功能在热图像中进行人体检测

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摘要

In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low-level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on self-created IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.
机译：在这项工作中，已经提出了红外人体探测方法。提出的解决方案着重于使用低级特征和检测人体部位。低级处理基于修改后的HOG（定向直方图）算法。首先，仅使用平方单元格，并且梯度的计算也得到了改进。接下来，从数据集IR（红外）图像创建的头部模型，也是人体模型。最后，使用最小距离分类器检查了概率矩阵。所提出的解决方案的新颖性集中于像素梯度和身体部位处理的结合，还提出了三个阶段的分类过程（头部建模，人体建模和分类器），以减少错误检测。实验是在自行创建的IR图像数据库上进行的，该数据库包含具有大多数可能困难情况的图像，例如重叠的人，不同的姿势，较小的人和高分辨率的人。使用Precision和Recall质量度量评估了所提出算法的性能。

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