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Real-time pedestrian detection via hierarchical convolutional feature

机译：通过分层卷积功能进行实时行人检测

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摘要

With the development of pedestrian detection technologies, existing methods can not simultaneously satisfy high quality detection and fast calculation for practical applications. Therefore, the goal of our research is to balance of pedestrian detection in aspects of the accuracy and efficiency, then get a relatively better method compared with current advanced pedestrian detection algorithms. Inspired from recent outstanding multi-category objects detector SSD (Single Shot MultiBox Detector), we proposed a hierarchical convolution based pedestrians detection algorithm, which can provide competitive accuracy of pedestrian detection at real-time speed. In this work, we proposed a fully convolutional network where the features from lower layers are responsible for small-scale pedestrians and the higher layers are for large-scale, which will further improve the recall rate of pedestrians with different scales, especially for small-scale. Meanwhile, a novel prediction box with a single specific aspect ratio is designed to reduce the miss rate and accelerate the speed of pedestrian detection. Then, the original loss function of SSD is also optimized by eliminating interference of the classifier to more adapt pedestrian detection while also reduce the time complexity. Experimental results on Caltech Benchmark demonstrates that our proposed deep model can reach 11.88% average miss rate with the real-time level speed of 20 fps in pedestrian detection compared with current state-of-the-art methods, which can be the most suitable model for practical pedestrian detection applications.
机译：随着行人检测技术的发展，现有方法无法同时满足高质量检测和快速计算的实际应用需求。因此，我们的研究目标是在行人检测的准确性和效率方面取得平衡，与目前的先进行人检测算法相比，获得一种相对更好的方法。受近期杰出的多类别物体检测器SSD（Single Shot MultiBox Detector）的启发，我们提出了一种基于分层卷积的行人检测算法，该算法可提供行人实时检测的竞争优势。在这项工作中，我们提出了一个完全卷积的网络，其中低层的特征负责小规模的行人，高层的特征负责大范围的行人，这将进一步提高不同规模的行人的召回率，尤其是小规模的行人规模。同时，设计了一种具有单个特定长宽比的新型预测框，以降低遗漏率并加快行人检测速度。然后，通过消除分类器的干扰来优化SSD的原始损失函数，以更适应行人检测，同时还降低了时间复杂度。在Caltech Benchmark上的实验结果表明，与当前最先进的方法相比，我们提出的深度模型在行人检测中的实时水平速度为20 fps时可以达到11.88％的平均丢失率。用于实际的行人检测应用。

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