In this paper, an efficient framework for counting pedestrians crossing a line of interest is proposed. Nowadays, the convolutional neural networks have very good results on pedestrian detection and tracking. However, the major drawback of the neural networks is that they require heavy computing resources. This limits the application of neural networks in low-cost systems. Thus, the low power consuming pedestrian counting systems with comparable performance are still important. To achieve this goal, the proposed method distils the pedestrian detection knowledge from a neural network to train the local binary patterns (LBP) cascade classifier model to detect pedestrians. Then a matching and tracking algorithm is used to count the number of pedestrians. An automaton was developed to eliminate the bouncing position of the detected pedestrians. The experimental comparisons show that, compared to Ma et al. and Felzenszwalb et al.’s methods, the quality of the line of interest counting of the proposed method is about the same and, at the same time, the execution time of the proposed method is much less.

在本文中，提出了一种有效的框架来计算穿过感兴趣线的行人。如今，卷积神经网络在行人检测和跟踪方面已经取得了很好的结果。但是，神经网络的主要缺点是它们需要大量的计算资源。这限制了神经网络在低成本系统中的应用。因此，具有可比性能的低功耗行人计数系统仍然很重要。为了实现这一目标，所提出的方法从神经网络中分发行人检测知识，以训练局部二进制模式。（LBP）级联分类器模型来检测行人。然后使用匹配和跟踪算法对行人数量进行计数。开发了自动机以消除检测到的行人的弹跳位置。实验比较表明，与Ma等人相比。与Felzenszwalb等人的方法相比，所提方法的兴趣线计数质量大致相同，并且所提方法的执行时间要短得多。