Smart Robotics Laboratory, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan.
Sensors (Basel). 2023 Apr 26;23(9):4285. doi: 10.3390/s23094285.
This study focuses on solving the correspondence problem of multiple moving objects with similar appearances in stereoscopic videos. Specifically, we address the multi-camera correspondence problem by taking into account the pixel-level and feature-level stereo correspondences, and object-level cross-camera multiple object correspondence. Most correspondence algorithms rely on texture and color information of the stereo images, making it challenging to distinguish between similar-looking objects, such as ballet dancers and corporate employees wearing similar dresses, or farm animals such as chickens, ducks, and cows. However, by leveraging the low latency and high synchronization of high-speed cameras, we can perceive the phase and frequency differences between the movements of similar-looking objects. In this study, we propose using short-term velocities (STVs) of objects as motion features to determine the correspondence of multiple objects by calculating the similarity of STVs. To validate our approach, we conducted stereo correspondence experiments using markers attached to a metronome and natural hand movements to simulate simple and complex motion scenes. The experimental results demonstrate that our method achieved good performance in stereo correspondence.
本研究专注于解决立体视频中多个外观相似的移动物体的对应问题。具体而言，我们通过考虑像素级和特征级立体对应以及对象级跨摄像机多个物体对应来解决多摄像机对应问题。大多数对应算法依赖于立体图像的纹理和颜色信息，因此难以区分外观相似的物体，例如穿着相似服装的芭蕾舞演员和公司员工，或者外观相似的农场动物，如鸡、鸭和牛。然而，通过利用高速摄像机的低延迟和高同步性，我们可以感知相似物体运动之间的相位和频率差异。在本研究中，我们提出使用物体的短期速度（STV）作为运动特征，通过计算 STV 的相似性来确定多个物体的对应关系。为了验证我们的方法，我们使用附着在节拍器和自然手部运动上的标记进行了立体对应实验，以模拟简单和复杂的运动场景。实验结果表明，我们的方法在立体对应中取得了良好的性能。
