School of Automotive Studies, Tongji University, Shanghai 201804, China.
Sensors (Basel). 2022 Jun 22;22(13):4702. doi: 10.3390/s22134702.
The detection of moving objects is one of the key problems in the field of computer vision. It is very important to detect moving objects accurately and rapidly for automatic driving. In this paper, we propose an improved moving object detection method to overcome the disadvantages of the RGB information-only-based method in detecting moving objects that are susceptible to shadow interference and illumination changes by adding depth information. Firstly, a convolutional neural network (CNN) based on the color edge-guided super-resolution reconstruction of depth maps is proposed to perform super-resolution reconstruction of low-resolution depth images obtained by depth cameras. Secondly, the RGB-D moving object detection algorithm is based on fusing the depth information of the same scene with RGB features for detection. Finally, in order to evaluate the effectiveness of the algorithm proposed in this paper, the Middlebury 2005 dataset and the SBM-RGBD dataset are successively used for testing. The experimental results show that our super-resolution reconstruction algorithm achieves the best results among the six commonly used algorithms, and our moving object detection algorithm improves the detection accuracy by up to 18.2%, 9.87% and 40.2% in three scenes, respectively, compared with the original algorithm, and it achieves the best results compared with the other three recent RGB-D-based methods. The algorithm proposed in this paper can better overcome the interference caused by shadow or illumination changes and detect moving objects more accurately.
运动目标检测是计算机视觉领域的关键问题之一。对于自动驾驶来说，准确快速地检测运动目标非常重要。本文提出了一种改进的运动目标检测方法，通过添加深度信息来克服基于 RGB 信息的方法在检测易受阴影干扰和光照变化影响的运动目标时的缺点。首先，提出了一种基于颜色边缘引导的深度图超分辨率重建的卷积神经网络（CNN），对深度相机获得的低分辨率深度图像进行超分辨率重建。其次，基于 RGB-D 的运动目标检测算法融合了同一场景的深度信息和 RGB 特征进行检测。最后，为了评估本文提出算法的有效性，分别使用 Middlebury 2005 数据集和 SBM-RGBD 数据集进行测试。实验结果表明，本文提出的超分辨率重建算法在六种常用算法中取得了最佳结果，本文提出的运动目标检测算法在三个场景中分别将检测精度提高了 18.2%、9.87%和 40.2%，与原始算法相比，与其他三种最新的基于 RGB-D 的方法相比，取得了最佳的结果。本文提出的算法可以更好地克服阴影或光照变化引起的干扰，更准确地检测运动目标。
