Embedded vision system development using 32-bit Single Board Computer and GNU/Linux - Nur Farhan, Kahar (Universiti Malaysia Perlis, 2011)
This research explores the usage of embedded system technology in developing a vision system to aid the process of monitoring traffic surveillance video. The increasing affordability of powerful processors and memory chips, availability of real-time operating systems, low complexity intelligent algorithms and the coming-of-age of system development software are the key factor that makes this development possible. An important application area where embedded vision system can potentially and advantageously replace most known cameras and computer solutions is visual traffic surveillance. Existing digital video surveillance systems provide the infrastructure only to capture, store and distribute video, while leaving the task of threat detection exclusively to human operators. The implementation of embedded vision system could reduce the need for human video scanning and has the additional effect of a more reliable system. This system will detect any existing stationary vehicle in its monitoring area and automatically convey the information to the operators. The development of embedded vision system is divided into two major phases which are the hardware integration and the software development. The main component for Embedded Vision System hardware design is an x86 TS5500 Single Board Computer (SBC), Logitech QuickCam Pro 4000 webcam, compact flash memory card, PCMCIA wireless network card, and a Desktop PC. The selection of x86 SBC is because of the function of size, speed, functionality, portability, lower cost, lower power consumption, ruggedness and supported by GNU/Linux OS. The overall software design is divided into three modules which are Image Acquisition, Image Processing and Object Detection, and Data Transmission module. The image processing algorithm includes color space conversion and motion analysis technique. In motion analysis, frame differencing, thresholding and convolution matrix filtering techniques are applied to detect and analyze movement in image sequence. Evaluations is performed on the processing time taken for overall smart camera operation and image processing process, monitoring the CPU utilization on the SBC’s processor during the program execution and observing the performance of the system implemented on different hardware platform. Overall embedded vision system processing time in SBC is 38.82 seconds compared to 6.09 seconds in desktop PC. The CPU processing speed and the size of short term memory (RAM) are the key factors that influence the performance of the embedded vision system. Processing speed comparison between TS5500 and TS7200 is being made and the result shows that TS7200 executes twice faster than TS5500. However, unsuitable camera driver obstruct the usage of TS7200 as the hardware platform. A significant discovery has been made in this research where the usage of shared memory is proven to save almost half of overall execution time for the embedded vision system. The stationary vehicle detection process is executed on the embedded vision system to evaluate the accuracy of detection made by the system. The experiment is made by using fifty samples of road image. From this experiment, the successful rate for stationary vehicle detection is 72%.