Spatial and Temporal Context Information Fusion Based Flying Objects Detection for Autonomous Sense and Avoid

In this paper, a novel flying object detection algorithm is proposed to assist with autonomous vision based sense and avoid system. Considering the specificities of flying objects detection, both the spatial and temporal context information are verified to be essential for ensuing the robustness of the algorithm. The algorithm is thus designed under a fusion architecture with the spatial and temporal context information. As for spatial context information extraction, the whole image is firstly sampled into a dense grid of image patches, then a pre-learned conditional random field (CRF) model is applied to generate the spatial probability map under a layered structure: CRF, sparse codes, bottom feature descriptors, and local image patches. As for temporal context information extraction, the motion cues are firstly detected by computing the forward back motion history image (FBMHI), then the foreground and background are further isolated by adaptive threshold selection. The probability map of the flying object presenting on the whole image is finally acquired by means of spatial and temporal context information fusion, and the flying object is detected on the basis of the spatial and temporal probability map. A number of aerial video sequences containing planes and drones are adopted for evaluating the effectiveness of the algorithm, and the results show the algorithm proposed in this paper performs favorable against other state-of-the-art techniques.