SVM classification of moving objects tracked by Kalman filter and Hungarian method

Fishery video data often require laborious visual analysis, therefore a video-based fish identification challenge is announced in the LifeCLEF campaign for automatic fish categorization and enumeration. We have elaborated a complex system to detect, classify and track objects (fishes) in underwater video by examining each image frame of it. For the detection process we used background subtraction and morphologic methods, and then our solution calculated bounding boxes based on object contours. We used Kalman filter to track the moving objects, but an additional matching method was required to pair the objects in consecutive time periods because of many fishes. We used Hungarian method for this matching problem. We categorized the detected fishes with C-SVC classifier, as an advanced SVM (Support Vector Machine) classifier. The classifier used high level descriptors, which are based on the extracted SURF vectors in each object. For optimization the C-SVC classifier we conducted a preliminary test, and we used the best parameters for teaching the classifier. We predicted the fish species in the official test video set, and our predictions were evaluated officially by NCS (Normalized Counting Score).