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학술대회 Spatio-Temporal Relationship Match: Video Structure Comparison for Recognition of Complex Human Activities
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유상원, J. K. Aggarwal
International Conference on Computer Vision (ICCV) 2009, pp.1593-1600
09MC3200, u-Robot 인지인프라 기술개발(주관 : u-City 환경기반 하이브리드 u-로봇 서비스 시스템 기술개발), 유원필
Human activity recognition is a challenging task, especially when its background is unknown or changing, and when scale or illumination differs in each video. Approaches utilizing spatio-temporal local features have proved that they are able to cope with such difficulties, but they mainly focused on classifying short videos of simple periodic actions. In this paper, we present a new activity recognition methodology that overcomes the limitations of the previous approaches using local features. We introduce a novel matching, spatio-temporal relationship match, which is designed to measure structural similarity between sets of features extracted from two videos. Our match hierarchically considers spatio-temporal relationships among feature points, thereby enabling detection and localization of complex non-periodic activities. In contrast to previous approaches to 'classify' videos, our approach is designed to 'detect and localize' all occurring activities from continuous videos where multiple actors and pedestrians are present. We implement and test our methodology on a newly-introduced dataset containing videos of multiple interacting persons and individual pedestrians. The results confirm that our system is able to recognize complex non-periodic activities (e.g. 'push' and 'hug') from sets of spatio-temporal features even when multiple activities are present in the scene. ©2009 IEEE.
KSP 제안 키워드
Continuous videos, Detection and Localization, Human activity recognition(HAR), Local feature, Non-periodic, Short Videos, Structure Similarity Index measure(SSIM), Temporal relationship, Video Structure, feature points, spatiotemporal features