Research On Object Feature Modeling And Applications For Battlefield Awareness Of Unmanned Aerial Vehicle

The perception of high-value targets in battle space is a foundational and keyproblem for an unmanned aerial vehicle (UAV) system to achieve autonomous control.As one of the essential contents of object information processing, object featuremodeling plays a connecting role in the process of UAV battlefield awareness. However,due to the high complexity and dynamics of the battlefield environment, the actualimages acquired by UAV usually have large uncertainty, which makes the establishmentof a reliable object model become a very challenging task. Under such a background,this dissertation focuses on the problem of object feature modeling, including threetypical applications, i.e. object feature matching, object category detection, and objecttracking. The main work and contributions of this dissertation are summarized asfollows:1) On the basis of in-depth analysis and discussion of the object feature modelingtheory with contextual information, the dissertation proposes three object featuremodeling frameworks. Aiming at the problems of feature extraction and expression inobject modeling, different detection methods and description types of local invariantfeatures are discussed in detail. Meanwhile, conditional random field (CRF) model,which has the unified framework for contextual information modeling, is analyzedemphatically. The abilities of using contextual information and key problems in objectfeature modeling for various extended forms of CRF model are studied.2) A novel feature point matching method that benefits from local-feature-groups(LFGs) and the multi-objective optimization theory is proposed, which can effectivelyreduce the mismatches caused by image ambiguities. Fisrt of all, LFGs are constructedthrough grouping affine invariant point features and regional features. Regional featuresmake a range constraint to point features, and geometrical ordering contextualinformation in each group is introduced to enhance robustness and discrimination ofLFGs. Then, feature point matching is formulated as a multi-objective optimizationproblem, and the optimal search is realized by using a novel discrete multi-objectiveparticle swarm optimization algorithm. Experimental results show that the proposedmethod can effectively improve robustness of object feature matching while comparedwith those without contextual information.3) In order to overcome the drawbacks of the classical CRF model for objectcategory detection, a contextual hierarchical part-driven CRF (CHCRF) model isproposed in the dissertation, which can effectively detect single-instance object categorywith notable intra-class variations. The graph structure, potential functions, parameterestimation and inference methods of CHCRF model are designed, respectively.Meanwhile, a weighted neighborhood structure is developed to capture the degree of correlation between connected nodes in the model. By using a two-layer hierarchicalformulation of labels, CHCRF model can effectively represent label-level context andobservation-level context simultaneously, and achieve unified modeling of objectcategory and its internal latent context.4) For multi-instance object detection, the dissertation proposes a multi-taskfactorial CRF (MFCRF) model to tightly combine the task of multiclass part labelingwith the task of binary object labeling. By fusing different types of labels, the MFCRFmodel can make full use of label-level contextual information. Experimental resultsshow that the modeling capability and detection performance of MFCRF model aresuperior to other existing extended forms of CRF model.5) To solve the drift problem of object model for visual tracking in complexdynamic scenes, a novel CRF model with spatial-temporal contextual information isproposed. By extending the classic CRF model in the time domain, the definition of thetime-domain neighborhood system is developed, and a spatial-temporal CRF (STCRF)model is proposed. The main characteristic of the model is that it can not only use thecompatibility of spatial neighborhood to deal with pose variations and image ambiguity,but also use the time domain smoothness constraint to model the continuity of objectlabels. Furthermore, an object tracking algorithm and a model update strategy based onlabeling results from STCRF model are designed to achieve robust object descriptionand tracking. Experimental results show that the proposed algorithm can significantlyreduce the average tracking error while compared with traditional feature matchingtracking methods.