Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106302
Title: Probabilistic reasoning for unique role recognition based on the fusion of semantic-interaction and spatio-temporal features
Authors: Yang, Chule
Yue, Yufeng
Zhang, Jun
Wen, Mingxing
Wang, Danwei
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Issue Date: 2019
Source: Yang, C., Yue, Y., Zhang, J., Wen, M., & Wang, D. (2019). Probabilistic reasoning for unique role recognition based on the fusion of semantic-interaction and spatio-temporal features. IEEE Transactions on Multimedia, 21(5), 1195-1208. doi:10.1109/TMM.2018.2875513
Journal: IEEE Transactions on Multimedia
Abstract: This paper deals with the problem of recognizing the unique role in dynamic environments. Different from social roles, the unique role refers to those who are unusual in their carrying items or movements in the scene. In this paper, we propose a hierarchical probabilistic reasoning method that relates spatial relationships between interested objects and humans with their temporal changes to recognize the unique individual. Two observation models, Object Existence Model (OEM) and Human Action Model (HAM), are established to support role inference by analyzing the corresponding semantic-interaction features and spatio-temporal features. Then, OEM and HAM results of each person are compared with the overall distribution in the scene, respectively. Finally, we can determine the role through the fusion of two observation models. Experiments are conducted in both indoor and outdoor environments concerning different settings, degrees of clutter, and occlusions. The results show that the proposed method can adapt to a variety of scenarios and outperforms other methods on accuracy and robustness, moreover, exhibiting stable performance even in complex scenes.
URI: https://hdl.handle.net/10356/106302
http://hdl.handle.net/10220/48869
ISSN: 1520-9210
DOI: 10.1109/TMM.2018.2875513
210580
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TMM.2018.2875513
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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