Scalable software architecture for on-line multi-camera video processing

In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea

Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms

Synote: development of a Web-based tool for synchronized annotations

This paper discusses the development of a Web-based media annotation application named Synote, which addresses the important issue that while the whole of a multimedia resource on the Web can be easily bookmarked, searched, linked to and tagged, it is still difficult to search or associate notes or other resources with a certain part of a resource. Synote supports the creation of synchronized notes, bookmarks, tags, links, images and text captions. It is a freely available application that enables any user to make annotations in and search annotations to any fragment of a continuous multimedia resource in the most used browsers and operating systems. In the implementation, Synote categorized different media resources and synchronized them via time line. The presentation of synchronized resources makes full use of Web 2.0 AJAX technology to enrich interoperability for the user experience. Positive evaluation results about the performance, efficiency and effectiveness of Synote were returned when using it with students and teachers for a number of undergraduate courses