Learning occlusion patterns using semantic phrases for object detection
Occlusion inference in image is a classical as well as difficult problem in computer vision. Most approaches model occlusion with different occlusion patterns in complex ways. In this paper, we propose a simple and efficient way to represent occlusion patterns called `occlusion pattern phrase', for example `dog occlude person'. These phrases model the occlusion patterns between two occluded objects, which can be used in applications like object detection and object classification. Here, we focus on using the occlusion pattern phrases in object detection. DPM is used to learn appearance models and a inference procedure is introduced to infer occlusion patterns with structural outputs. Unlike other methods, our method produces not only location boxes for different objects, but also demonstrates their occlusion relations. A new dataset with well annotated occlusion pattern images collected from Pascal VOC2007 and search engines like Bing and Google is introduced in this paper. Experiments show that our method outperforms the baseline and the occlusion pattern phrases can describe the relations between objects as we expect. In the future, we will explore the use of occlusion pattern phrases in scene understanding.