This paper introduces a flexible hardware and software architecture for a smart video sensor. This sensor has been applied in a video surveillance application where some of these video sensors are deployed, constituting the sensory nodes of a distributed surveillance system. In this system, a video sensor node processes images locally in order to extract objects of interest, and classify them. The sensor node reports the processing results to other nodes in the cloud (a user or higher level software) in the form of an XML description. The hardware architecture of each sensor node has been developed using two DSP processors and an FPGA that controls, in a flexible way, the interconnection among processors and the image data flow. The developed node software is based on pluggable components and runs on a provided execution run-time. Some basic and application-specific software components have been developed, in particular: acquisition, segmentation, labeling, tracking, classification and feature extraction. Preliminary results demonstrate that the system can achieve up to 7.5 frames per second in the worst case, and the true positive rates in the classification of objects are better than 80%.

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The popularity of 3d graphics has grown exponentially in the latest years, which has lead to an increase in the range of applications where this kind of representations are used. The monitoring of industrial processes is an area where this type of simulation is rarely used, being much more common to show the information in traditional 2d interfaces.An application for data visualization in maritime container terminals is introduced here. We have developed a modular system adaptable to any stacked objects problem. This paper describes the architecture of our system, its features, and the graphics techniques applied to achieve a high frame rate and keep it independent of the data size.