Real Time Action Recognition Using Histograms of Depth Gradients and Random Decision Forests

Rahmani, Hossein and Mahmood, Arif and Huynh, Du Q. and Mian, Ajmal (2014) Real Time Action Recognition Using Histograms of Depth Gradients and Random Decision Forests. In: IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 626-633. ISBN 9781479949854

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
127079
Deposited By:
Deposited On:
24 Aug 2018 08:36
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Oct 2020 07:56