MSM-HOG: A flexible trajectory descriptor for rigid body motion recognition

Abstract

This paper proposes a flexible descriptor for representing 6-D rigid body motion trajectories, which not only shows strong invariances and descriptive ability but also achieves satisfactory results in both recognition accuracy and efficiency. 6-D rigid body motion trajectories are first transformed into the Multi-layer Self-similarity Matrices (MSM) representation. The MSM is the combination of the square similarity matrices in three layers, which captures both local and global spatiotemporal features of the trajectories. Next, the Histogram of Oriented Gradients (HOG) features extracted from the MSM representation are concatenated as the final MSM-HOG trajectory descriptor. Then we train the Support Vector Machine (SVM) classifier with the linear kernel for multicalss motion recognition. Finally, rigid body motion recognition experiments on two public datasets are conducted to verify the effectiveness and efficiency of the proposed method.