Maritime assets such as merchant and navy ships, ports, and harbors are targets of terrorist attacks as evidenced by the USS Cole bombing. Conventional methods of securing maritime assets to prevent attacks are manually intensive and error prone. To address this shortcoming, we are developing a decision support system that shall alert security personnel to potential attacks by automatically processing maritime surveillance video. An initial task that we must address is to accurately classify maritime objects from video data, which is our focus in this paper. Object classification from video images can be problematic due to noisy outputs from image processing. We approach this problem with a novel technique that exploits maritime domain characteristics and formulates it as a graph of spatially related objects. We then apply a case-based collective classification algorithm on the graph to classify objects. We evaluate our approach on river traffic video data that we have processed. We found that our approach significantly increases classification accuracy in comparison with a conventional (i.e., nonrelational) alternative.
Case-Based Collective Inference for Maritime Object Classification
2009
16 pages
Report
Keine Angabe
Englisch
Physical & Chemical Oceanography , Computers, Control & Information Theory , Military Intelligence , Photographic Techniques & Equipment , Detection & Countermeasures , Classification , Decision support systems , Image processing , Inshore areas , Rivers , Surveillance , Video images , Algorithms , Artificial intelligence , Automation , Graphs , Harbors , Ports(Facilities) , Ships , Symposia , Target detection , Target recognition , Threat evaluation , Tracking , Maritime video surveillance , Maritime object classification , Maritime activity analysis workbench , Case-based reasoning , Case-based collective classification , River traffic video data , Object classifiers , Local algorithms , Global algorithms , Case-based collective inference , Iterative classification algorithm , Potomac river
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