The ability to create super high-resolution video is becoming relative easy to do either through a single high-definition video camera or panoramic video that automatically stitches multiple views together. As an example of the former, the motion picture industry now has 6000 × 4000 pixel full-rate video cameras available. This means that supporting region-of-interest cropping will become more important in the future. In this article, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression-compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Fortunately, video standards like MPEG-2 specify the format of a compliant stream, and not the algorithm to get there. As a result, there is an opportunity to allow system researchers and implementers ways to optimize for applications. We show various fundamental tradeoffs that are made in order to support region-of-interest cropping with super high-resolution video which we received from a local motion-picture firm.

For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3-D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3-D feature point-rich target to all cameras, then using the known geometry of a 3-D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3-D target's feature points. As each 3-D target position establishes a distinct coordinate frame, cameras that view more than one 3-D target position compute translations and rotations relating different positions' coordinate frames and share the transform data with neighbors to facilitate realignment of all cameras to a single coordinate frame. Compared to other localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it requires communication only to determine simultaneous target viewings and for passing transform data. Additionally, our solution requires only pairwise view overlaps of sufficient size to see the 3-D target and detect its feature points, while also giving camera positions in meaningful units. We evaluate our algorithm in both real and simulated smart camera networks. In the real network, position error is less than 1 ('') when the 3-D target's feature points fill only 2.9% of the frame area.

The ability to create very high-resolution video is becoming relative easy to do today, either through a single high definition video camera or panoramic video stitched from multiple cameras. This means that supporting region-of-interest cropping will become more important in the future. In this paper, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Using data from a motion-picture industry video camera, we show that the proposed approach, while adding a small amount of overhead, can be used to efficiently crop large resolution video into a smaller stream.