LBP-Flow and Hybrid Encoding for Real-Time Water and Fire Classification

Konstantinos Avgerinakis, Panagiotis Giannakeris, Alexia Briassouli, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 412-418

In this work, we focus on the challenging problem of real-world dynamic scene understanding, where videos contain dynamic textures that have been recorded in the 'wild'. These videos feature large illumination variations, complex motion, occlusions, camera motion, as well as significant intra-class differences. We address these issues by introducing a novel dynamic texture descriptor, the "Local Binary Pattern-flow" (LBP-flow), which is shown to be able to accurately classify dynamic scenes whose complex motion patterns are difficult to separate using existing local descriptors, or which cannot be modelled by statistical techniques. The descriptor statistics are encoded with Fisher vector, while a neural network follows to reduce the dimensionality and increase the discriminability of the final descriptor. The proposed algorithm leads to a highly accurate spatio-temporal descriptor which achieves a very low computational cost enabling the deployment of our descriptor in applications