Abstract

We describe the design and implementation of a hybrid intelligent surveillance system consisting of an embedded system and a personal computer (PC)-based system. The embedded system performs some of the image processing tasks and sends the processed data to a PC. The PC tracks persons and recognizes two-person interactions by using a grayscale side-view image sequence captured by a stationary camera. Based on our previous research, we explored the optimum division of tasks between the embedded system and the PC, simulated the embedded system using dataflow models in Ptolemy, and prototyped the embedded systems in real-time hardware and software using a 16-bit CISC microprocessor. This embedded system processes one frame image in 89 ms, which is within three frame-cycle periods for a 30 Hz video system. In addition, the real-time embedded system prototype uses 5.7 Kbytes of program memory, 854 Kbytes of internal data memory and 2 Mbytes external DRAM.

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