A constant-time region-based memory allocator for embedded systems with unpredictable length array generation

Sánchez, Jordi; Benet, G.; Simó, José

doi:10.1109/etfa.2010.5640956

Cited by 2 publications

(1 citation statement)

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“…Data flows from lower layers to upper layers, processing higher level data on each step, until real knowledge about the state of the environment is obtained. In [ 17 ] and [ 18 ] some preliminary results obtained in this project were presented. In this paper, we present the details of the architecture involving the lowest level layers, which provides object detection, classification and tracking analysis to upper layers.…”

Section: Introductionmentioning

confidence: 99%

Video Sensor Architecture for Surveillance Applications

Sánchez

Benet

Simó

2012

Sensors

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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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Section: Introductionmentioning

confidence: 99%