Partially Occluded Object Detection By Finding The Visible Features And Parts

We address the problem of partially occluded object detection by implementing a model which includes latent visibility flags that are attached to cells and parts of a Deformable Part Model (DPM) [1]. A visibility flag indicates whether an image portion is part of a target object or part of an occluder. To compute the visibility flags and the score of the detector simultaneously, we maximize a concave objective function that is composed of the following four terms: (1) the detection scores of visible cells and parts, (2) a cell-to-cell consistency term which encourages neighboring cells to have the same visibility flags, (3) a cell-to-part consistency term which encourages compatible labeling among overlapping cells and parts, and (4) a penalty term for cells and parts that are labeled as occluded. The maximization of the concave objective function is done using the Alternating Direction Method of Multipliers (ADMM). By removing scores of occluded cells and parts from the final detection score we significantly improve detection performance on partially occluded pedestrians. In experiments we show that our system outperforms the standard DPM and other state-of-art methods on a benchmark database of partially occluded pedestrians.