Simultaneous Recognition of Human Action and Its Location Estimation Based on Multiview Hough Voting

• Published in: Electronics and communications in Japan 2016-01, Vol.99 (1), p.42-52
• Main Authors: HARA, KENSHO, HIRAYAMA, TAKATSUGU, MASE, KENJI
• Format: Article
• Language: English
• Subjects: action recognition; Automation; Hough transform; Human; multiview video; Position (location); random forests; Recognition; Surveillance; Tracking (position); Tracking errors; Voting
• ISSN: 1942-9533; 1942-9541
• DOI: 10.1002/ecj.11773

SUMMARY Remote surveillance of large‐scale equipment such as power plants and building complexes is important to prevent serious attacks and troubles. Automatic human action recognition can reduce the burdens of the surveillance. Multiview video is useful for human action recognition, because it provides robustness to the changes of people's appearance by orientation and occlusion. One problem of conventional multiview action recognition is that it requires both detection and tracking before action recognition. Human pose and motion vary depending on the person's action, and such variances may cause detection and tracking error. To solve this problem, previous work has proposed simultaneous action recognition and location estimation for single‐view videos using Hough voting. In this paper, we extend the Hough voting approach to simultaneous multiview action recognition and location estimation. Our proposed method independently casts votes for the action labels and spatiotemporal reference positions of actions in each view and integrates them using homographical transformations in the multiview extension. We evaluated our method and confirmed that it achieved high accuracy in action recognition and location estimation. The contribution of this paper is that it enables multiview action recognition without prior human detection and tracking.