Niels C. Haering

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We propose a multi-level video event detection methodology and apply it to animal hunt detection in wildlife documentaries. The proposed multi-level approach has three levels. The rst level extracts color, texture, and motion features, and detects moving object blobs. The mid-level employs a neural network to verify whether the moving object blobs belong to(More)
We compare features and classification methods to locate deciduous trees in images. From this comparison we conclude that a backpropagation neural network achieves better classification results than the other classifiers we tested. Our analysis of the relevance of 51 features from seven feature extraction methods based on the graylevel co-occurrence matrix,(More)
The safety and security of transportation infrastructure has become the focus of increased attention in the post-9/11 era. Concerns in this area include the possibility of terrorist activity directed at travelers and/or property. As the leader in intelligent video, ObjectVideo is developing intelligent capabilities specifically tailored to meet the needs of(More)
We propose a three-level video event detection algorithm and apply it to animal hunt detection in wildlife documentaries. The rst level extracts texture, color, and motion features, and detects motion blobs. The mid-level employs a neural network to verify whether the motion blobs belong to objects of interest. This level also generates shot summaries in(More)
We propose a three-level video-event detection methodology and apply it to animal-hunt detection in wildlife documentaries. The first level extracts color, texture, and motion features, and detects shot boundaries and moving object blobs. The mid-level employs a neural network to determine the object class of the moving object blobs. This level also(More)
We propose a method for recovering the affine geometry of a dynamically textured plane from a video sequence taken by an uncalibrated, fixed, perspective camera. Some instances of approximately planar surfaces that are coated with a dynamic texture include large water bodies, (such as lakes and oceans), heavy traffic, dense crowds, escalators, and foliage(More)
We propose a multi-level video event detection methodology and apply it to animal hunt detection in wildlife documentaries. The proposed multi-level approach has three levels. The rst level extracts color, texture, and motion features, and detects moving object blobs. The mid-level employs a neural network to verify whether the moving object blobs belong to(More)