Abstract
In this paper, we present a spatiotemporal wavelet correlogram (STWC) as a new feature for human action recognition (HAR) in videos. The proposed feature benefits from a different approach with respect to bag of visual words, interest point detection and descriptor representation method. The new approach requires neither motion estimation (tracking) nor background/foreground subtraction. STWC is generated more efficiently compared to the state-of-the-art HAR methods and achieves comparable results. STWC utilizes the multi-scale, multi-resolution property of wavelet transform and considers the correlation of wavelet coefficients. It is generated by computing spatiotemporal correlogram of quantized wavelet coefficients. These coefficients are computed using 3D wavelet decomposition and a simple quantization method. Based on the present findings, recommendations are made for the selection of the richest wavelet subbands to compute STWC.
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