CAJ | 학술논문

人体行为识别是计算机视觉领域的一个热点问题，然而目前大部分算法都是仅使用RGB或深度视频序列，很少将它们结合起来进行行为识别。由于它们都有各自的优点且信息是互补的，因此文中研究深度图像和RGB图像的特性，不仅提出两种鲁棒的深度图和RGB图像上的行为描述算法，而且将它们有效融合，进一步结合多个不同核函数的SVM分类器在具有挑战性的DHA数据集上对它们进行评估。大规模实验结果表明，文中提出的行为描述算法性能比一些最具代表性算法的性能更好。同时，深度数据和RGB图像融合后算法性能得到进一步提高，比单独使用深度数据或RGB图像的性能更好，且具有较好的区分性和鲁棒性。
인체행위식별시계산궤시각영역적일개열점문제，연이목전대부분산법도시부사용RGB혹심도시빈서렬，흔소장타문결합기래진행행위식별。유우타문도유각자적우점차신식시호보적，인차문중연구심도도상화RGB도상적특성，불부제출량충로봉적심도도화RGB도상상적행위묘술산법，이차장타문유효융합，진일보결합다개불동핵함수적SVM분류기재구유도전성적DHA수거집상대타문진행평고。대규모실험결과표명，문중제출적행위묘술산법성능비일사최구대표성산법적성능경호。동시，심도수거화RGB도상융합후산법성능득도진일보제고，비단독사용심도수거혹RGB도상적성능경호，차구유교호적구분성화로봉성。
Human behavior recognition is a hot issue in computer vision. However, most of the existing algorithms only use RGB or depth video sequence, and few of them are combined for behavior recognition. Due to their own advantages and complementary information, the characteristics of depth images and RGB images are studied, and two kinds of robust descriptors and some fusion schemes for them are proposed in this paper. Then, the support vector machine classifiers with different kernels are adopted. Results of extensive experiments on the challenging DHA dataset show that the accuracies of the proposed descriptors are higher than those of the state-of-the-art algorithms. Meanwhile, the performance of the algorithm with the combination of depth information and RGB is improved, and it is better than that of the algorithm with sole descriptor. Moreover,the proposed descriptors have strong robustness, discriminability and stability.