Abstract: A digital video processing method and an apparatus thereof are provided. The method for processing digital images received in the form of compressed video streams comprising the step of determining a region intensity histogram (RIH) based on information on motion compensation of inter frames. The RIH information is obtained based on the motion compensation values of inter frames, and the RIH information is a good indicator of motion information of a video scene. Also, since the RIH information is quite a good indicator of intensity of the video scene, video streams having similar intensities can be effectively searched by searching for similar video scenes based on the RIH information obtained by the digital video processing method.

Abstract: Provides a system for detecting an intersection between more than one panoramic video sequence and detecting the orientation of the sequences forming the intersection. Video images and corresponding location data are received. If required, the images and location data is processed to ensure the images contain location data. An intersection between two paths is then derived from the video images by deriving a rough intersection between two images, determining a neighborhood for the two images, and dividing each image in the neighborhood into strips. An identifying value is derived from each strip to create a row of strip values which are then converted to the frequency domain. A distance measure is taken between strips in the frequency domain, and the intersection is determined from the images having the smallest distance measure between them.

Abstract: A method describes activity in a video sequence. The method measures intensity, direction, spatial, and temporal attributes in the video sequence, and the measured attributes are combined in a digital descriptor of the activity of the video sequence.

Abstract: An object activity modeling method which can efficiently model complex objects such as a human body is provided. The object activity modeling method includes the steps of (a) obtaining an optical flow vector from a video sequence; (b) obtaining the probability distribution of the feature vector for a plurality of video frames, using the optical flow vector; (c) modeling states, using the probability distribution of the feature vector; and (d) expressing the activity of the object in the video sequence based on state transition. According to the modeling method, in video indexing and recognition field, complex activities such as human activities can be efficiently modeled and recognized without segmenting objects.