This paper describes a new method for the moving object detection using the IMU sensor and instance image segmentation. In the proposed method, the feature points are extracted by the detector, and the initial fundamental matrix is calculated from the IMU data. Next, the epipolar line is used to classify the extracted feature points. From the background feature point matching, fundamental matrix is calculated iteratively to minimize the error of classification. After the feature point classification, image segmentation is used to enhance the quality of the classification result. The proposed method is implemented and tested with real-world driving videos, and compared with the previous works.

本文介绍了一种使用IMU传感器和实例图像分割进行运动物体检测的新方法。在所提出的方法中，特征点由检测器提取，并且从IMU数据中计算出初始基本矩阵。接下来，将对极线用于对提取的特征点进行分类。根据背景特征点匹配，迭代地计算基本矩阵，以最大程度地减少分类误差。在特征点分类之后，使用图像分割来提高分类结果的质量。所提出的方法已在实际驾驶视频中实施和测试，并与以前的工作进行了比较。