Object tracking on FPGA-based smart cameras using local oriented energy and phase features

This paper presents the use of local oriented energy and phase features for real-time object tracking on smart cameras. In our proposed system, local energy features are used as spatial feature set for representing the target region while the local phase information are used for estimating the motion pattern of the target region. The motion pattern information of the target region is used for displacement of search area. Local energy and phase features are extracted by filtering the incoming images with a bank of complex Gabor filters. The effectiveness of the chosen feature set is tested using a mean-shift tracker. Our experiments show that the proposed system can significantly enhance the performance of the tracker in presence of photometric variations and geometric transformation. The real-time implementation of the system is also described in this paper. To achieve the desired performance, a hardware/software co-design approach is pursued. Apart from mean-shift vector calculation, the other blocks are implemented on hardware resources. The system was synthesized onto a Xilinx Virtex-5 XC5VSX50T using Xilinx ML506 development board and the implementation results are presented.