Moving object detection is a challenging task in video surveillance. Recently proposed Robust Principal Component
Analysis (RPCA) can recover the outlier patterns from the low-rank data under some mild conditions. However, the
ℓ-penalty in RPCA doesn’t work well in moving object detection because the irrepresentable condition is often not
satisfied. In this paper, a method based on total variation (TV) regularization scheme is proposed. In our model, image
sequences captured with a static camera are highly related, which can be described using a low-rank matrix. Meanwhile,
the low-rank matrix can absorb background motion, e.g. periodic and random perturbation. The foreground objects in the
sequence are usually sparsely distributed and drifting continuously, and can be treated as group outliers from the
highly-related background scenes. Instead of ℓ-penalty, we exploit the total variation of the foreground. By minimizing
the total variation energy, the outliers tend to collapse and finally converge to be the exact moving objects. The
TV-penalty is superior to the ℓ-penalty especially when the outlier is in the majority for some pixels, and our method
can estimate the outlier explicitly with less bias but higher variance. To solve the problem, a joint optimization function
is formulated and can be effectively solved through the inexact Augmented Lagrange Multiplier (ALM) method. We
evaluate our method along with several state-of-the-art approaches in MATLAB. Both qualitative and quantitative results
demonstrate that our proposed method works effectively on a large range of complex scenarios.
In order to achieve real-time extraction of feature point in the target detection and tracking system, we propose improved SIFT feature extraction algorithm based on FPGA hardware platform, which focuses on Pyramid structures and Gaussian convolution, at the same time, through the reasonable selection of algorithm parameter and fixed-point number , we can ensure the precision of the algorithm. In addition, we can achieve coordination between the various modules by multiplexing the SRAM. Experimental results indicate that the improved SIFT algorithm on FPGA hardware platform has high stability, low algorithm complexity and high accuracy, meanwhile, shows up good real-time capability.