Multi-feature fusion behavior recognition algorithm based on spatiotemporal weighting

doi:10.3969/j.issn.1000-1565.2019.01.016

Abstract

Abstract: Behavior recognition is a basic application of machine vision. At present, most algorithms for behavior recognition are based on the features from space domain, or other algorithms are simply merge the features on time domain and space domain. Thus these methods restrain the capability of representation. To deal with this problems, we propose a time-space domain feature fusion method. In this method, we introduce the spatiotemporal weight strategy into time-space pyramid. By doing this, we can break the limitations of feature space, integrate the features from the two dimensions together. The experiments show that our spatiotemporal weighting method on multi-feature fusion can improve the accuracy of behavior recognition.

Key words: multi-feature, spatiotemporal weighting, feature fusion, behavior recognition

CLC Number:

TP319

WANG Sile, WANG Mingyu, YANG Wenzhu, CHEN Liping, CHEN Xiangyang. Multi-feature fusion behavior recognition algorithm based on spatiotemporal weighting[J]. Journal of Hebei University (Natural Science Edition), 2019, 39(1): 93-98.

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