Chen Zhongyu, Zhao Rong, Guo Xindong, Xie Jianbin, Han Xie
School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
Shanxi Key Laboratory of Machine Vision and Virtual Reality, Taiyuan 030051, China.
Sensors (Basel). 2024 Apr 30;24(9):2859. doi: 10.3390/s24092859.
Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds.
使用自由移动相机进行运动目标检测（MOD）是计算机视觉中的一项具有挑战性的任务。为了提取运动目标，大多数研究都集中在前景和背景之间的运动特征差异上，这对于具有相对规则运动和变化的动态场景效果良好。然而，在现实世界场景中经常会出现光照突然变化和遮挡，并且相机还可能进行平移、倾斜、旋转和抖动等操作，导致运动特征出现局部不规则变化和全局不连续性。这种复杂多变的场景给运动目标检测带来了很大困难。为了解决这个问题，本文提出了一种新的MOD方法，该方法有效地利用局部和全局视觉信息进行前景/背景分割。具体来说，在全局方面，为了支持更广泛的相机运动，在丰富帧间匹配对后，将相对帧间变换优化为以全局形式参考中间帧的绝对变换。使用空间变换器网络（STN）对全局变换进行微调。在局部方面，为了解决动态背景场景的问题，通过利用当前帧与局部背景模型之间的像素差异以及局部空间变化的一致性来优化前景目标检测。然后，使用光流分割方法组合空间信息，提高目标信息的精度。实验结果表明，在CDNET2014、FBMS - 59和CBD数据集上，我们的方法与现有最先进方法相比，检测准确率提高了超过1.5%。它在诸如阴影、光照突然变化、相机抖动、遮挡和移动背景等具有挑战性的场景中显示出显著的有效性。
