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 in the wind. Under the assumption of translational dynamic textures, we propose a direct algorithm for the estimation of the inter-frame elation that does not require explicit identification of texels or movetons. In addition, we develop a general algorithm for recovering the affine geometry of homogeneous dynamic textures by identifying a constraint on the expected values of motion magnitudes. We report experimental results on several real videos of dynamic texture found in the world.

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 generates shot descriptors that combine features from the first level and inferences from the mid-level. The shot descriptors are then used by the domain-specific inference process at the third level to detect video segments that match the user defined event model. The proposed approach has been applied to the detection of hunts in wildlife documentaries. Our method can be applied to different events by adapting the classifier at the intermediate level and by specifying a new event model at the highest level. Event-based video indexing, summarization, and browsing are among the applications of the proposed approach.

We propose a three-level video event detection algorithm and apply it to animal hunt detection in wildlife documentaries. The first 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 terms of intermediate-level descriptors which combine low-level features from the first level and contain results of mid-level, domain specific inferences made on the basis of shot features. The shot summaries are then used by a domain-specific inference process at the third level to detect the video segments that contain events of interest, e.g., hunts. Event based video indexing, summarization and browsing are among the applications of the proposed approach.