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Fig. 13. The control authentication protocol for distributed gesture recognition.

Figure 13 The control authentication protocol for distributed gesture recognition.

Related Figures (16)

Fig. 1. Service-oriented software architecture example—tracking application.
Fig. 1. Service-oriented software architecture example—tracking application.
Fig. 2. (a) The three phases for software modeling using a service-oriented architecture and the application services: (b) atomic services and (c) composite services.
Fig. 2. (a) The three phases for software modeling using a service-oriented architecture and the application services: (b) atomic services and (c) composite services.
Fig. 3. The (a) target object, (b) data flow, and (c) control flow aspects in metamodeling for dis- tributed cameras using GME.
Fig. 3. The (a) target object, (b) data flow, and (c) control flow aspects in metamodeling for dis- tributed cameras using GME.
Fig. 4. The service graph aspect in metamodeling for distributed cameras using GME.
Fig. 4. The service graph aspect in metamodeling for distributed cameras using GME.
Fig. 5. The basic architecture of the target-centric programming framework.
Fig. 5. The basic architecture of the target-centric programming framework.
Fig. 6. A service graph example: vehicle tracking system.
Fig. 6. A service graph example: vehicle tracking system.
Fig. 7. The pseudocode for the (a) service discovery protocol (SDP); (b) service scheduler; (c) atomic and (d) composition constraint satisfaction.
Fig. 7. The pseudocode for the (a) service discovery protocol (SDP); (b) service scheduler; (c) atomic and (d) composition constraint satisfaction.
Fig. 8. The block diagram of the proposed migration methodology.
Fig. 8. The block diagram of the proposed migration methodology.
Fig. 9. The algorithm architecture of the single-node smart camera system.
Fig. 9. The algorithm architecture of the single-node smart camera system.
Fig. 10. The transmitted data size for different candidate stages of our distributed gesture recog: nition system.
Fig. 10. The transmitted data size for different candidate stages of our distributed gesture recog: nition system.
Fig. 11. The (a) system finite-state automaton, (b) control flow, and (c) data flow graphs of the distributed gesture recognition system with two cameras.
Fig. 11. The (a) system finite-state automaton, (b) control flow, and (c) data flow graphs of the distributed gesture recognition system with two cameras.
Fig. 12. The (a) control and (b) data flow service graph for the composite intraframe services in the recognized by both state.
Fig. 12. The (a) control and (b) data flow service graph for the composite intraframe services in the recognized by both state.
Fig. 14. The pseudocode for the (a) authority control protocol and (b) message preparation.
Fig. 14. The pseudocode for the (a) authority control protocol and (b) message preparation.
Fig. 15. The 8-byte header for data and control messages.
Fig. 15. The 8-byte header for data and control messages.
Fig. 16. A snapshot of the prototype program for our distributed gesture recognition system.
Fig. 16. A snapshot of the prototype program for our distributed gesture recognition system.
Fig. 17. The snapshots from the distributed gesture recognition system using (a) single-camera and (b) multiple-camera algorithms.
Fig. 17. The snapshots from the distributed gesture recognition system using (a) single-camera and (b) multiple-camera algorithms.
Related topics:
Computer ScienceDistributed ComputingDesignComputer HardwareGesture RecognitionSoftwareSmart CameraReal time and Embedded SystemsDynamic ReconfigurationService Orientation
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