Architecture for Dynamic Allocation of Computer Vision Tasks

The use of reconfigurable computer vision architecture for image processing tasks is an important and challenging application in real time systems with limited resources. It is an emerging field as new computing architectures are developed, new algorithms are proposed and users define new emerging applications in surveillance. In this paper, a computer vision architecture capable of reconfiguring the processing chain of computer vision algorithms is summarised. The processing chain consists of multiple computer vision tasks, which can be distributed over various computing units. One key characteristic of the designed architecture is graceful degradation, which prevents the system from failure. This system characteristic is achieved by distributing computer vision tasks to other nodes and parametrizing each task depending on the specified quality-of-service. Experiments using an object detector applied to a public dataset are presented.