Volume Local Binary Patterns are a well-known feature type to describe object characteristics in the spatiotemporal domain. Apart from the computation of a binary pattern further steps are required to create a discriminative feature. In this paper we propose different computation methods for Volume Local Binary Patterns. These methods are evaluated in detail and the best strategy is shown. A Random Forest is used to find discriminative patterns. The proposed methods are applied to the well-known and publicly available KTH dataset and Weizman dataset for singleview action recognition and to the IXMAS dataset for multiview action recognition. Furthermore, a comparison of the proposed framework to state-of-the-art methods is given.
Baumann et al. REF applied Volume Local Binary Patterns (VLBP) for human action recognition.
Computation strategies for volume local binary patterns applied to action recognition
2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Computer Science