Real-time object recognition using local features on a DSP-based embedded system

Abstract

In the last few years, object recognition has become one of the most popular tasks in computer vision. In particular, this was driven by the development of new powerful algorithms for local appearance based object recognition. So-called “smart cameras” with enough power for decentralized image processing became more and more popular for all kinds of tasks, especially in the field of surveillance. Recognition is a very important tool as the robust recognition of suspicious vehicles, persons or objects is a matter of public safety. This simply makes the deployment of recognition capabilities on embedded platforms necessary. In our work we investigate the task of object recognition based on state-of-the-art algorithms in the context of a DSP-based embedded system. We implement several powerful algorithms for object recognition, namely an interest point detector together with an region descriptor, and build a medium-sized object database based on a vocabulary tree, which is suitable for our dedicated hardware setup. We carefully investigate the parameters of the algorithm with respect to the performance on the embedded platform. We show that state-of-the-art object recognition algorithms can be successfully deployed on nowadays smart cameras, even with strictly limited computational and memory resources.