AbstractsComputer Science

Object tracking plays an important role in many applications, such as video surveillance, human-computer interaction, vehicle navigation, and robot control. It is generally defined as a task of estimating the location of an object over a sequence of images. In practical applications, there are many factors that make the task complex such as illumination variation, appearance change, shape deformation, partial occlusion, and camera motion. Moreover, lots of these applications require real-time response. Therefore, the development of real-time working algorithms is of essential importance. In order to accomplish such a challenging task, a real-time tracking system has been developed and proposed in this thesis. In this thesis, a solution to the tracking task is proposed based on consideration of efficient implementation as priority, and several critical issues are resolved as follows. At first, a hardware-friendly tracking framework is designed, which is implemented on field-programmable gate array (FPGA) technology, and compatible with very large scale integration (VLSI) technology. This framework, named multiple candidate regeneration (MCR), is developed as a simple but high-speed and high-efficiency searching algorithm. The basic idea was inherited from the particle filter (PF) but the algorithm has been greatly modified from the original particle filter so that it can be implemented on VLSI hardware very efficiently. The important difference between MCR and PF is that the MCR is developed by simplifying the visual tracking task and considering the simple hardware implementation. It can be considered as an efficient searching strategy instead of an intensity estimation method. In the development, several problems that may limit the hardware performance have been solved, such as complex computation, data transmission and utilization of hardware resources. The proposed architecture achieved 150 frame per second (f/s) on FPGA, and can reach about 900 f/s if it is implemented on VLSI with on-chip image sensor. This solution has several advantages. First, it works at high frame rate, which can enhance the effect of localization. It also meets the requirement of higher processing speed in some complex intelligence systems, which seems difficult to achieve by conventional solutions. Second, the system can be extended to be useful in many applications because of its flexibility. Third, since the processing speed is faster than the frame rate, there is still large space for further improving the ability of the system without losing real-time performance. The system was implemented on a Terasic DE3 FPGA board. Under the operating frequency of 60 MHz, the experimental system achieved a processing ability of 0.8 ms per frame in tracking a 64 * 64 scale object image in 640 * 480-pixel size video sequences. In tracking algorithms, how to represent the target image is of particular importance because it greatly influences the tracking performance under certain tracking framework. Color, edge, and texture are typical attributes…