By Bose R., Sugumaran V.
Facts research and mining applied sciences aid deliver company intelligence into organizational choice help platforms (DSS). whereas a myriad of knowledge research and mining applied sciences are commercially to be had this present day, agencies are seeing a turning out to be hole among strong garage (data warehouse) structures and the enterprise clients' skill to investigate and act successfully at the info they comprise. We contend that to slender this hole successfully, a knowledge research and mining setting is required tnat can assemble and make on hand to be used lots of those applied sciences, that may aid company clients with varied backgrounds, and with which the clients can paintings conveniently.
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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.