Robust object tracking using local oriented energy features and its hardware/software implementation

This paper presents the use of local oriented energy features for real-time object tracking on embedded vision systems. Local oriented energy features are extracted using complex Gabor filters. Filtering is carried out across multiple channels with different frequencies and orientations. The effectiveness of the chosen feature set is tested using a mean-shift tracker. Our experiments show that adding local oriented energy features can significantly enhance the performance of the tracker in presence of photometric variations and geometric transformation. The realtime implementation of the system is also described in this paper. To achieve the desired performance, a hardware/software co-design approach is pursued. Multi-channel Gabor filtering, Local Oriented Energy Feature and Feature histogram Computation is implemented on hardware while mean-shift vector calculation is performed on a processor. The system was synthesized onto a Xilinx Virtex-5 XC5VSX50T using Xilinx ML506 development board and the implementation results are presented.