In this paper, a nonoverlapping multi-camera and people re-identification algorithm is proposed. It applies inflated major color features for re-identification to reduce computation time. The inflated major color features can dramatically improve efficiency while retaining high accuracy of object re-identification. The proposed method is evaluated over a wide range of experimental databases. The accuracy attains upwards of 40.7% in Rank 1 and 84% in Rank 10 on average, while it obtains three to 15 times faster than algorithms reported in the literature. The proposed algorithm has been implemented on a SOC-FPGA platform to reach 50 FPS with 1280×720 HD resolution and 25 FPS with 1920×1080 FHD resolution for real-time processing. The results show a performance improvement and reduction in computation complexity, which is especially ideal for embedded platform.

In recent years, the demand for multimedia mobile battery-operated devices has created a need for low power implementation of video compression. Many compression standards require the discrete cosine transform (DCT) function to perform image/video compression. For this reason, low power DCT design has become more and more important in today's image/video processing. This paper presents a new power-efficient Hybrid DCT architecture which combines Loeffler DCT and binDCT in terms of special property on luminance and chrominance difference. We use Synopsys PrimePower to estimate the power consumption in a TSMC 0.25-µm technology. Besides, we also adopt a novel quality assessment method based on structural distortion measurement to measure the quality instead of peak signal to noise rations (PSNR) and mean squared error (MSE). It is concluded that our Hybrid DCT offers similar quality performance to the Loeffler, and leads to 25% power consumption and 27% chip area savings.