Dynamic texture recognition and localization in machine vision for outdoor environments

This work focuses on detecting and localizing a wide range of dynamic textures in video sequences captured by surveillance cameras. Their reliable and robust analysis constitutes a challenging task for traditional computer vision methods, due to barriers like occlusions, the highly non-rigid nature of the moving entities and the complex stochastic nature of their motions. In order to address these issues, a novel hybrid framework is introduced, combining representations on both a local and global scale. A new, handcrafted local binary pattern (LBP)-flow descriptor with Fisher encoding is initially used to effectively capture low level texture dynamics, and a neural network (NN) is deployed after it to obtain a higher level, deeper and more effective representation scheme, capable of robustly discriminating even challenging dynamic texture classes. A novel localization scheme, based on multi-scale superpixel clustering is introduced, in order to detect texture patterns on local and global scales, inside and throughout sequential video frames. Experiments on various challenging benchmark datasets prove our method's efficacy and generality, as remarkable recognition and localization accuracy rates are achieved at a low computational cost, making it appropriate for real world outdoor applications.