A spindle model for contextual object detection

Recent progresses on visual object detection manifest the significance of context information (e.g., scene semantic, object interactions, geometric cues, etc.) for boosting the recognition performance. Particularly, the object pose information has been widely exploited as important contextual cue in human-object interactions (HOIs). This paper proposes a spindle model to utilize pose information in multi-class object interactions, which is not limited to HOIs, for contextual object detection. The structural support vector machine (SSVM) algorithm is induced to learn the proposed structured model. Moreover, we present an efficient method based on K-L divergence (KLD) to refine the pose context features from potentially huge number of dimensions. The experimental results on PASCAL VOC 2007 dataset demonstrate that the proposed model can effectively improve performance w.r.t. the state-of-the-art methods for object detection tasks.