Segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses

We propose a method that detects and segments multiple, partially occluded objects in images. A part hierarchy is defined for the object class. Whole-object segmentor and part detectors are learned by boosting shape oriented local image features. During detection, the part detectors are applied to the input image. All the edge pixels in the image that positively contribute to part detection responses are extracted. A joint likelihood of multiple objects is defined based on the part detection responses and the object edges. Computing the joint likelihood includes an inter-object occlusion reasoning that is based on the object silhouettes extracted with the whole-object segmentor. By maximizing the joint likelihood, part detection responses are grouped, merged, and assigned to multiple object hypotheses. The proposed approach is applied to the pedestrian class, and evaluated on two public test sets. The experimental results show that our method outperforms the previous ones.