Real time action recognition using histograms of depth gradients and random decision forests

H. Rahmani, Arif Mahmood, Du Huynh, Ajmal Mian

Research output: Chapter in Book/Conference paperConference paper

76 Citations (Scopus)

Abstract

We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second. © 2014 IEEE.
Original languageEnglish
Title of host publicationIEEE Winter Conference on Applications of Computer Vision, WACV 2014
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages626-633
ISBN (Print)9781479949854
DOIs
Publication statusPublished - 2014
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, United States
Duration: 24 Mar 201426 Mar 2014

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs
Period24/03/1426/03/14

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Rahmani, H., Mahmood, A., Huynh, D., & Mian, A. (2014). Real time action recognition using histograms of depth gradients and random decision forests. In IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 626-633). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2014.6836044