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首页> 外文期刊>Transportation planning and technology >Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm
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Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

机译：基于累积二进制Haar特征和改进的深度信念网络算法的行人安全行人检测

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摘要

In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on an improved deep belief network (DBN) classification algorithm is proposed. An improved method of input is constructed using a Restricted Bolzmann Machine (RBM) with T distribution function visible layer nodes, which can convert information on pedestrian features to a Bernoulli distribution, and the Bernoulli distribution can then be used for recognition. In addition, a middle layer of the RBM structure is created, which achieves data transfer between the hidden layer structure and keeps the key information. Finally, the cost-sensitive Support Vector Machine (SVM) classifier is used for the output of the classifier, which could address the class-imbalance problem. Extensive experiments show that the improved DBN pedestrian detection method is better than other shallow classic algorithms, and the proposed method is effective and sufficiently feasible for pedestrian detection in complex urban environments.
机译：为了提高交通安全并保护行人，提出了一种改进的高效的自动驾驶辅助系统行人检测方法。首先，提出了一种改进的累积二进制Haar特征提取算法。在此新颖功能中，Haar功能仅保留由二进制Haar功能命名的序数关系。然后，该特征引入了“本地二进制模式”（LBP）的思想，将几个相邻的二进制Haar特征组合在一起，以提高区分能力并降低照明效果。接下来，提出了一种基于改进的深度信念网络（DBN）分类算法的行人分类方法。使用具有T分布函数可见层节点的受限Bolzmann机器（RBM）构造一种改进的输入方法，该方法可以将行人特征信息转换为Bernoulli分布，然后可以将Bernoulli分布用于识别。另外，创建了RBM结构的中间层，该中间层实现了隐藏层结构之间的数据传输并保留了关键信息。最后，将成本敏感的支持向量机（SVM）分类器用于分类器的输出，这可以解决类不平衡问题。大量实验表明，改进的DBN行人检测方法优于其他浅层经典算法，该方法对于复杂城市环境中的行人检测是有效且充分可行的。

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