Wide area monitoring for community and city can be a very challenging engineering task due to its scale and heterogeneity in sensor, algorithm, and visualization levels. Multi-modal cameras and algorithms have to be fused into compact presentation for a single operator to actively and effectively respond to anomaly events and jeopardy. This paper presents a distributed and scalable video surveillance system, subcontracted by intelligent surveillance components (ISCs) and visualization surveillance components (VSCs) in compliance with functional labors. The ISCs are high-level algorithms applying computer vision for behavioral analysis of human and vehicles. The VSCs constitute a multi-tier subsystem to visualize fused results of messages, key frames, streaming videos and geographic context information. The system helps the operator to focus attention on interested events gathered from distrusted ISCs and presented by VSCs on map and three-dimensional homographic views. Robustness and effectiveness of the system has been demonstrated by a test run of real scenarios deployed in a campus.
Super-resolution is an important method to reconstruct high-resolution images from low-resolution images. In this paper, a manifold learning algorithm based on two-dimensional locality preserving projection (2D-LPP) is proposed for face image super-resolution. The 2D-LPP detects the intrinsic manifold structure of high space and preserves the structure in low space by projection. The projection approach in the 2D-LPP resolves the out-of-sample problem in embedding-based manifold learning methods, and improves the speed in reducing the dimension of a new sample data. Moreover, the 2D-LPP preserves more accurate manifold structure by directly operating on 2D images rather than flattened 1D vector as PCA and LPP does. Extensive experiments are conducted on the AR and FERET databases. Experimental results show that the proposed method performs better than PCA based super-resolution in both PSNR and time efficiency.
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