Abstract:
A scalable method and apparatus is described to provide personalized interactive visualization of a plurality of compressed image data to a plurality of concurrent users. A plurality of image sources are digitally processed in the compressed domain to provide controllable enhanced user-specific interactive visualization with support for adjustment in viewing parameters such frame-rate, field of view, resolution, color format, viewpoint and bandwidth.
Abstract:
A system for tracking at least one object is disclosed. The system includes a plurality of communicatively connected visual sensing units configured to capture visual data related to the at least one object The system also includes a manager component communicatively connected to the plurality of visual sensing units. The manager component is configured to assign one visual sensing unit to act as a visual sensing unit in a master mode and at least one visual sensing unit to act as a visual sensing unit in a slave mode. The manager component is further configured to transmit at least one control signal to the plurality of visual sensing units, and receive the visual data from the plurality of visual sensing units.
Abstract:
A scalable method and apparatus is described to provide personalized interactive visualization of a plurality of compressed image data to a plurality of concurrent users. A plurality of image sources are digitally processed in the compressed domain to provide controllable enhanced user-specific interactive visualization with support for adjustment in viewing parameters such frame-rate, field of view, resolution, color format, viewpoint and bandwidth.
Abstract:
Method for tracking an object recorded within a selected frame of a sequence of frames of video data, using a plurality of layers, where at least one object layer of the plurality of layers represents the object includes initializing layer ownership probabilities for pixels of the selected frame using a non-parametric motion model, estimating a set of motion parameters of the plurality of layers for the selected frame using a parametric maximization algorithm and tracking the object. The non-parametric motion model is optical flow and includes warping the mixing probabilities, the appearances of the plurality of layers, and the observed pixel data from the pixels of the preceding frame to the pixels of the selected frame to initialize the layer ownership probabilities for the pixels of the selected frame.
Abstract:
A method and apparatus for video surveillance is disclosed. In one embodiment, a sequence of scene imagery representing a field of view is received. One or more moving objects are identified within the sequence of scene imagery and then classified in accordance with one or more extracted spatio-temporal features. This classification may then be applied to determine whether the moving object and/or its behavior fits one or more known events or behaviors that are causes for alarm.
Abstract:
A method and apparatus for detecting objects (e.g., bags, vehicles, etc.) left in a field of view are disclosed. A long-term representation and a short-term representation of the field of view are constructed, and a difference between the long-term representation and the short-term representation is calculated. One or more criteria may optionally be applied to this difference to determine whether the difference represents an object that was left in the field of view.
Abstract:
A method and apparatus for performing video surveillance of a field of view is disclosed. In one embodiment, a method for performing surveillance of the field of view includes monitoring the field of view and detecting a moving object in the field of view, where the motion is detected based on a spatio-temporal signature (e.g., a set of descriptive feature vectors) of the moving object.
Abstract:
A sentient system combines detection, tracking, and immersive visualization of a cluttered and crowded environment, such as an office building, terminal, or other enclosed site using a network of stereo cameras. A guard monitors the site using a live 3D model, which is updated from different directions using the multiple video streams. As a person moves within the view of a camera, the system detects its motion and tracks the person's path, it hands off the track to the next camera when the person goes out of that camera's view. Multiple people can be tracked simultaneously both within and across cameras, with each track shown on a map display. The track system includes a track map browser that displays the tracks of all moving objects as well as a history of recent tracks and a video flashlight viewer that displays live immersive video of any person that is being tracked.
Abstract:
A method and apparatus for detecting objects (e.g., bags, vehicles, etc.) left in a field of view are disclosed. A long-term representation and a short-term representation of the field of view are constructed, and a difference between the long-term representation and the short-term representation is calculated. One or more criteria may optionally be applied to this difference to determine whether the difference represents an object that was left in the field of view.
Abstract:
This invention is a method of decoding a stream of video image data transmitted as independent image frames consisting of plural marcoblocks transmitted in a nonsequential order. The method defines a sub-frame corresponding to a proper subset of the full frame. The method determines if a currently received macroblock is within the sub-frame. The method decodes the sub-frame. The sub-frame may be decoded at less than or equal to the frame rate of the video image data. A table has one entry for each macroblock that stores a transmission order within the video frame for the corresponding macroblock. The method determine if a current macroblock is within the sub-frame by reading the table. Each macroblock consists of a plurality of contiguous blocks and includes luminance data for any included blocks and chrominance data for the macroblock as a whole. The method optionally decodes the luminance data for each included block and ignores the chrominance data. The method decodes the sub-frame employing only data prior to an end of data marker or the end of a data group allocated to that block, and ignores data following an end of data marker. The method may also decode a full frame of video image data at a full frame decode rate less than the sub-frame decode rate. The method preferably employs a digital camcorder to generate the stream of video image data and a notebook computer for decoding and display.