Simultaneous Foreground Detection and Classification With Hybrid Features

Jaemyun Kim, Adin Ramirez Rivera, Byungyong Ryu, Oksam Chae; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3307-3315

In this paper, we propose a hybrid background model that relies on edge and non-edge features of the image to produce the model. We encode these features into a coding scheme, that we called Local Hybrid Pattern (LHP), that selectively models edges and non-edges features of each pixel. Furthermore, we model each pixel with an adaptive code dictionary to represent the background dynamism, and update it by adding stable codes and discarding unstable ones. We weight each code in the dictionary to enhance its description of the pixel it models. The foreground is detected as the incoming codes that deviate from the dictionary. We can detect (as foreground or background) and classify (as edge or inner region) each pixel simultaneously. We tested our proposed method in existing databases with promising results.