关键词: 动作的分割与识别, 关键帧提取, 尺度不变局部特征点, 支持向量机

Abstract: The observations, decompositions and records of motions were usually accomplished through artificial means during the processes of motion analyses. This method had a heavy workload, and the efficiency was very low. A novel method was put forward herein to segment and recognize continuous human motions automatically based on machine vision for mechanical assembly operations. First, the content-based dynamic key frame extraction technology was utilized to extract key frames from video stream, and then automatic segmentations of actions were implemented. Further, the SIFT feature points of the region of interested were extracted, on the basis of which the characteristic vectors of the key frame were derived. Finally, a classifier was constructed based on SVM to classify feature vectors, and the motion types were identified according to the classification results. Experimental results demonstrate that the proposed method achieves correct recognition rates of 96% on sample videos which were captured on the assembly lines.

Key words: segmentation and recognition of motion, key frame extraction, scale invariant feature transform(SIFT) feature points, support vector machine(SVM)