Abstract
In this paper, we present a gait recognition method using convolutional features and histograms of temporal gradients. The method comprises three stages, namely the convolutional stage, temporal gradient stage and classification stage. In the convolutional stage, the video frames are convolved with a set of pre-learned filters. This is followed by the temporal gradient stage. In this stage, the gradient of each convolved frame in time axis is calculated. Unlike histograms of oriented gradients that accumulate the gradients in the spatial domain, the proposed histogram of temporal gradients encodes the gradients in the spatial and temporal domain. The histogram of temporal gradients captures the gradient patterns of every pixel over the temporal axis throughout the video sequence. By doing so, the feature encodes both spatial and temporal information in the gait cycle. Finally, in the classification stage, a majority voting classification with Euclidean distance is performed for gait recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods.
Original language | English |
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Article number | 012051 |
Journal | Journal of Physics: Conference Series |
Volume | 1502 |
Issue number | 1 |
DOIs | |
Publication status | Published - 17 Jun 2020 |
Externally published | Yes |
Event | International Conference on Telecommunication, Electronic and Computer Engineering 2019, ICTEC 2019 - Melaka, Malaysia Duration: 22 Oct 2019 → 24 Oct 2019 |
ASJC Scopus subject areas
- General Physics and Astronomy