“Motion based Feature Point Selection Algorithms with KLT Tracker and Its Hardware Implementation”

Surveillance camera systems play an important role for creating safe and secure society. Especially, real-time motion detection is a key to detect abnormal scenes. So, we picked up KLT (Kanade-Lucas-Tomasi) tracker and tried to implement a system. However, there are still many problems in accuracy and system cost. This paper proposes a score control by weighted mask and an adaptive feature point interval algorithms to increase accuracy of object detection. Moreover, to implement these algorithms onto a low cost FPGA, hardware architectures, such as weighted value generation circuit, insert position calculation circuit and feature point data update circuit, are proposed. Evaluation results shows that the proposed algorithm can detect motion vectors with high accuracy for various surveillance scenes. Moreover, hardware implementation results show that the proposed architecture attains real-time processing with around 20% FPGA resources.