Evaluating spatiotemporal interest point features for depth-based action recognition

• Published in: Image and vision computing Vol. 32; no. 8; pp. 453 - 464
• Main Authors: Zhu, Yu, Chen, Wenbin, Guo, Guodong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2014
• Subjects: Action recognition; Descriptors; Detectors; Evaluation; Feature fusion; Feature recognition; Human; Imaging; Recognition; RGB-D sensor; Spatiotemporal interest point (STIP); State of the art; STIP feature refinement; STIP features; Support vector machines; Three dimensional; Action recognition; STIP feature refinement; Evaluation; RGB-D sensor; Detectors; Feature fusion; Descriptors; Spatiotemporal interest point (STIP); STIP features
• DOI: 10.1016/j.imavis.2014.04.005
• ISSN: 0262-8856

Human action recognition has lots of real-world applications, such as natural user interface, virtual reality, intelligent surveillance, and gaming. However, it is still a very challenging problem. In action recognition using the visible light videos, the spatiotemporal interest point (STIP) based features are widely used with good performance. Recently, with the advance of depth imaging technology, a new modality has appeared for human action recognition. It is important to assess the performance and usefulness of the STIP features for action analysis on the new modality of 3D depth map. In this paper, we evaluate the spatiotemporal interest point (STIP) based features for depth-based action recognition. Different interest point detectors and descriptors are combined to form various STIP features. The bag-of-words representation and the SVM classifiers are used for action learning. Our comprehensive evaluation is conducted on four challenging 3D depth databases. Further, we use two schemes to refine the STIP features, one is to detect the interest points in RGB videos and apply to the aligned depth sequences, and the other is to use the human skeleton to remove irrelevant interest points. These refinements can help us have a deeper understanding of the STIP features on 3D depth data. Finally, we investigate a fusion of the best STIP features with the prevalent skeleton features, to present a complementary use of the STIP features for action recognition on 3D data. The fusion approach gives significantly higher accuracies than many state-of-the-art results. •A comprehensive evaluation of STIP based features on depth-based action recognition•Two schemes to refine STIP features for a deeper understanding of their behaviors•A fusion approach is developed which outperforms many state-of-the-art methods.