In order to solve the problems such as background interference, illumination variation and occlusion between targets in people counting of actual public scene videos, a new people counting method combined with feature map learning and first-order dynamic linear regression was proposed. Firstly, the mapping model of feature map between the Scale-Invariant Feature Transform (SIFT) feature of image and the target true density map was established, and the feature map containing target and background features was obtained by using aforementioned mapping model and SIFT feature. Then, according to the facts of the less background changes in the monitoring video and the relatively stable background features in the feature map, the regression model of people counting was established by the first-order dynamic linear regression from the integration of feature map and the actual number of people. Finally, the estimated number of people was obtained through the regression model. The experiments were performed on the datasets of MALL and PETS2009. The experimental results show that, compared with the cumulative attribute space method, the mean absolute error of the proposed method is reduced by 2.2%, while compared with the first-order dynamic linear regression method based on corner detection, the mean absolute error and the mean relative error of the proposed method are respectively reduced by 6.5% and 2.3%.

The traditional video super-resolution reconstruction algorithm cannot preserve the details of the image edge effectively while removing the noise. In order to solve this problem, a video super-resolution reconstruction algorithm combining adaptive regularization term with multi-order derivative data item was put forward. Based on the regularization reconstruction model, the multi-order derivative of the noise, which described the statistical characteristics of the noise well, was introduced into the improved data item; meanwhile, Total Variation (TV) and Non-Local Mean (NLM) which has better denoising effect were used as the regularization items to constrain the reconstruction process. In addition, to preserve the details better, the coefficient of regularization was weighted adaptively according to the structural information, which was extracted by the regional spatially adaptive curvature difference algorithm. In the comparison experiments with the kernel-regression algorithm and the clustering algorithm when the noise variance is 3, the video reconstructed by the proposed algorithm has sharper edge, the structure is more accurate and clear; and the average Mean Squared Error (MSE) is decreased by 25.75% and 22.50% respectively; the Peak Signal-to-Noise Ratio (PSNR) is increased by 1.35 dB and 1.14 dB respectively. The results indicate that the proposed algorithm can effectively preserve the details of the image while removing the noise.

To address the problem of the coincidence of the position and size for a same face cannot be guaranteed in different results due to the scale transformation in face detection, research has been done by the method of merging windows in face-detection employing statistical training. And a method based on Euclidean distance was proposed too, without considering the case of error-detected and undetected faces. Judging circle and Euclidean distance were employed to merge face-detection windows according to the distribution of center coordinates. Verifying experiments of the method were conducted according to different pictures, and the experimental results proved the method simple and effective.