Realtime object matching with robust dominant orientation templates

Most of conventional object matching methods are based on comparing the local features, which are too computational demanding to achieve realtime performance on object detection in videos. Recently, Dominant Orientation Templates (DOT) method was proposed to make online feature detection and comparison feasible. However, it still suffers the problem of fragility due to the noise and partial occlusions. To efficiently tackle these problems, we introduce the similarity map to store the matching scores of individual grids in each sliding window, which is used to further denoise and infer the occlusion map. The promising experimental results demonstrate that proposed approach improves the robustness, which outperforms DOT assuredly.