Novelty Detection in Stored Video StreamWorld Applied Sciences Journal
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This paper aims at developing a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. Apply the newly developed model to the application area of object recognition in stored video. This framework model is trained on input video stream and tested on the video stream with some novel objects. This proposed paper enhances the existing work by following ways: i) Novel objects can be reliably detected when they partially enter the frames. ii) Illumination changes in outdoor video recordings do not effect the novelty detection. In this paper a new method of novelty detection have been developed. This methodology is significantly different to the use of other novelty detection techniques like novelty detection with softmax, novelty detection with auto–associator .
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