Context-based abnormal object detection using the fully-connected conditional random fields

•We propose a new approach to abnormal object detection.•We formulate abnormal object detection as a joint labeling problem.•The statistical relationships between objects are embedded in the Euclidean space.•The proposed model considers the fully-connected relationships between objects.•The proposed model achieves the state-of-the-arts performances. The contextual information plays an important role in computer vision, particularly in object detection and scene understanding. The existing contextual models use only the relationship between normal objects and natural scenes, and thus there still remains a difficult problem in detection of abnormal objects. This paper proposes an abnormal object detection model using the fully-connected conditional random fields to integrate the contextual information such as the co-occurrence and geometric relationships between objects. With this formulation, the proposed model combines the co-occurrence, spatial interaction between objects, and scale information. To this end, we use a feature embedding technique to find a geometry that reflects the statistical relationship in the pairwise term. Abnormal object detection is solved by using probabilistic variational inference such as the mean field approximation. Experimental results show that the proposed abnormal object detection model achieves significant improvement over the state-of-the-art models on the out-of-context dataset and abnormal object dataset.