Background modeling using adaptive properties of hybrid features

Jaemyun Kim, Adin Ramirez Rivera, Byeongwoo Kim, Kaushik Roy, Oksam Chae

Abstract: In this paper, we propose Local Adaptive Hybrid Pattern (LAHP) to use intrinsic properties, both edge and color, of each pixel adaptively (inspired from LHP based methods) while using a single feature representation (inspired from LOBSTER). The proposed LAHP encodes edge and color information, and an adaptive factor based on gradient magnitude together as a single feature. We introduce a way to calculate the code distance of LNESP to reduce sensitive to edge distortions. Furthermore, we modify ADM (Adaptive Dictionary Model) to manage LAHP and extend a feature matching scheme of ADM to adjacent background models to reduce sensitivity to background motions. Results show promising performance than other methods in standard datasets.