Object Detection with Contextual Inference

In this paper, art object detection system that utilizes contextual relationships between individually detected objects to improve the overall detection performance is introduced. The first contribution in this work is the modelling of real world object relationships (beside, on, near, etc.) that can be probabilistically inferred using measurements in the 2D image space. The other contribution is the assignment of final labels to the detected objects by maximizing a scene probability function that is defined jointly using both individual object labels and their pairwise spatial relationships. The most consistent scene configuration is obtained by solving the maximization problem using linear optimization. Experiments on two different office data sets showed that incorporation of the real world spatial relationships as contextual information improved the overall detection performance.