By James R. Wilcox
This specified factor contains the various striking paintings initially offered on the ACM Multimedia convention 2003 (ACM MM 2003). The convention got 255 submissions, of which forty three high quality papers have been approved for presentation. of those papers, the Technical application Chairs invited a dozen authors to post more advantageous models in their papers to this distinctive factor. those papers went via a rigorous assessment strategy, and we're chuffed to offer 4 actually striking papers during this specialissue. as a result of hugely aggressive assessment method and constrained area, many glorious papers couldn't be authorised for this exact factor. despite the fact that, a few of them are being forwarded for attention as destiny common papers during this magazine.
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Additional info for ACM Transactions on Multimedia Computing, Communications and Applications (May)
The list on the right is a list of events that the video sensor has captured. The cameras are controlled by a number of parameters which are described in the next section. The video window on the left allows events to be played back. In addition, it allows basic queries to be run on the video database. 3. 11 wireless networking to network the sensor to the aggregating node. To maximize the scalability of the system, we have implemented a simple change detection filtering algorithm. The basic goal of the motion filtering is ACM Transactions on Multimedia Computing, Communications and Applications, Vol.
Among these are algorithms to perform the DCT algorithm, quantization and Huffman encoding. For test data, we use a sample image in 4:2:0 YUV planar form taken from our lab and use it to test just the compression component. For each test, we compressed the image 300 times in a loop and averaged the measured compression times. As shown in Table II, we are able to achieve real-time compression of 320 × 240 pixel video using the Intel Performance Primitives. More importantly, it takes approximately 1/3 or 1/6 the time using the primitives compared with using a non-Intel-specific software optimized algorithm on the Bitsy and Stargate, respectively.
Otherwise, they are just capturing data in futility. In order to demonstrate the usefulness of video-based sensor-networking applications, we have implemented a scalable video surveillance system using the Panoptes video sensor. The system allows video sensors to connect to it automatically and allows the sensors to be controlled through the user interface. The video surveillance system consists of a number of components, including the video sensor, a video aggregating node, and a client interface.