Temporal Order-Preserving Dynamic Quantization For Human Action Recognition From Multimodal Sensor Streams

Keywords

Human action recognition; Multimodal feature fusion; Temporal dynamic quantization; Temporal order

Abstract

Recent commodity depth cameras have been widely used in the applications of video games, business, surveillance and have dramatically changed the way of human-computer interaction. They provide rich multimodal information that can be used to interpret the human-centric environment. However, it is still of great challenge to model the temporal dynamics of the human actions and great potential can be exploited to further enhance the retrieval accuracy by adequately modeling the patterns of these actions. To address this challenge, we propose a temporal order-preserving dynamic quantization method to extract the most discriminative patterns of the action sequence. We further present a multimodal feature fusion method that can be derived in this dynamic quantization framework to exploit different discriminative capability of features from multiple modalities. Experiments based on three public human action datasets show that the proposed technique has achieved state-of-theart performance.

Publication Date

6-22-2015

Publication Title

ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval

Number of Pages

99-106

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2671188.2749340

Socpus ID

84962436354 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84962436354

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