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We present a directional-edge-based object tracking system based on a field-programmable gate array (FPGA) that can process 640 &#x00D7; 480 resolution video sequences and provide the location of a predefined object in real time. Inspired by biological principle, directional edge information is used to represent the object features. Multiple candidate regeneration, a statistical method, has been developed to realize the tracking function, and online learning is adopted to enhance the tracking performance. Thanks to the hardware-implementation friendliness of the algorithm, an object tracking system has been very efficiently built on an FPGA, in order to realize a real-time tracking capability. At the working frequency of 60 MHz, the main processing circuit can complete the processing of one frame of an image (640 &#x00D7; 480 pixels) in 0.1 ms in high-speed mode and 0.8 ms in high-accuracy mode. The experimental results demonstrate that this system can deal with various complex situations, including scene illumination changes, object deformation, and partial occlusion. Based on the system built on the FPGA, we discuss the issue of very large-scale integrated chip implementation of the algorithm and self initialization of the system, i.e., the autonomous localization of the tracking object in the initial frame. Some potential solutions to the problems of multiple object tracking and full occlusion are also presented.

A Directional-Edge-Based Real-Time Object Tracking System Employing Multiple Candidate-Location Generation