Pedestrian detection plays an important role in automatic driving system and intelligent robots, and has made great progress in recent years. Identifying the pedestrians from confused planar objects is a challenging problem in the field of pedestrian recognition. In this article, we focus on the 2D fake pedestrian identification based on light-field (LF) imaging and convolutional neural network (CNN). First, we expand the previous dataset to 1500 samples, which is a mid-size dataset for LF images in all public LF datasets. Second, a joint CNN classification framework is proposed, which uses both RGB image and depth image (extracted from the LF image) as input. This framework can fully mine 2D feature information and depth feature information from corresponding images. The experimental results show that the proposed method is efficient to identify the fake pedestrian in a 2D plane and achieves a recognition accuracy of 97.0%. This work is expected to be used in recognition of 2D fake pedestrian and may help researchers solve other computer vision problems.

行人检测在自动驾驶系统和智能机器人中起着重要作用，并且近年来取得了长足的进步。在行人识别领域，从混乱的平面物体中识别行人是一个具有挑战性的问题。在本文中，我们重点研究基于光场（LF）成像和卷积神经网络（CNN）的二维假行人识别。首先，我们将先前的数据集扩展到1500个样本，这是所有公共LF数据集中LF图像的中型数据集。其次，提出了一个联合的CNN分类框架，该框架同时使用RGB图像和深度图像（从LF图像中提取）作为输入。该框架可以从对应的图像中充分挖掘2D特征信息和深度特征信息。实验结果表明，该方法能有效识别二维平面上的假行人，识别率达97.0％。预期这项工作将用于识别2D假行人，并可能有助于研究人员解决其他计算机视觉问题。