Action Recognition in the Longwave Infrared and the Visible Spectrum Using Hough Forests

Abstract

Action recognition in surveillance systems has to work 24/7 under all kinds of weather and lighting conditions. Towards this end, most action recognition systems only work in the visible spectrum which limits their general usage to daytime applications. In this work Hough forests are applied to the longwave infrared spectrum which can capture humans both in the dark and in daylight. Further, Integral Channel Features which have shown promising results in the spatial domain are applied to the spatio-temporal domain and are incorporated into the Hough forest approach. This approach is evaluated on a new outdoor dataset containing different violent and non-violent actions recorded in the visible and infrared spectrum. It is further shown that for the visible spectrum the proposed approach achieves state-of-the-art results on the KTH and i3DPost dataset.

Multimodal action recognition using variational‐based Beta‐Liouville hidden Markov models

“…In this section, we evaluate the proposed model on two challenging AR datasets: IR infrared action recognition (InfAR) [35] and multispectral IOSB [36]. These datasets were chosen for their unique position to enable the evaluation of the proposed model with others in the field as well as investigate the multispectral fusion aspect of the paper.…”

“…This dataset consists of visible and IR action videos that have been recorded on a sunny summer day of ten people; eight males and two females in the age range of 31.25.7 [36]. We test our proposed algorithm on three classes of the dataset; namely, film, point, and throw.…”