Part-aware CNN for Pedestrian Detection

Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in the applications such as monitoring system and automatic drive. Although it has been exhaustively studied over the past decade, the occlusion situation remains a very challenging problem. In order to deal with this problem, one convincing method is to utilize the parts based methods for the visible parts information, and furthermore to estimate the pedestrian position. Many part-based pedestrian detection methods have been proposed in recent years. According to our analyses, clumsy part combining process have always been the problems to limit pedestrian detection performance. In this paper, we propose Part-aware CNN to solve this problem. In this study, we focus on the part detector combination phase, which including a brand new method to reform the part detectors to the convolutional layer of the CNN and optimize the whole pipeline by fine-tuning the CNN. In experiments, it shows the astonishing effectiveness of optimization and robustness of occlusion handling.

行人检测是计算机视觉中的一项重要任务。近年来，它广泛应用于监控系统和自动驾驶等应用中。尽管在过去十年中已经进行了详尽的研究，但遮挡情况仍然是一个极具挑战性的问题。为了解决这个问题，一种令人信服的方法是利用基于部件的方法获取可见部件信息，并进一步估计行人位置。近年来已经提出了许多基于部件的行人检测方法。根据我们的分析，笨拙的部件组合过程一直是限制行人检测性能的问题。在本文中，我们提出了部件感知卷积神经网络（Part - aware CNN）来解决这个问题。在这项研究中，我们专注于部件检测器组合阶段，包括一种全新的方法，将部件检测器重构到卷积神经网络的卷积层，并通过微调卷积神经网络来优化整个流程。在实验中，它显示出优化的惊人效果以及遮挡处理的鲁棒性。