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首页> 外文会议>Computational Intelligence for Multimedia Signal and Vision Processing, 2009. CIMSVP '09 >A car detection system based on hierarchical visual features
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A car detection system based on hierarchical visual features

机译：基于分层视觉特征的汽车检测系统

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In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy is developed. Tested on the UIUC car database, the proposed method achieves better classification results than some of the existing car detection approaches.
机译：在本文中，我们解决了在静止图像中检测和定位汽车的问题。所提出的汽车检测系统基于分层特征检测器，其中处理单元将抑制神经元分流。为了减少训练时间和网络的复杂性，第一层中的分流抑制神经元被实现为定向非线性滤波器，而第二层中的神经元具有可训练的参数。实现了一种多分辨率处理方案，以检测不同大小的汽车，并在检测阶段减少误报的数量，开发了一种自适应阈值策略。通过对UIUC汽车数据库的测试，与现有的某些汽车检测方法相比，该方法获得了更好的分类结果。

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