Real-time covariance tracking architecture optimizations for embedded systems

Abstract

International audienceCovariance matching techniques have recently grown in interest due to their good performances for object retrieval, detection and tracking. By mixing color and texture information in a compact representation, it can be applied to various kinds of objects (textured or not, rigid motion or not).Unfortunately, the original version requires heavy computations, and is difficult to execute in real-time. This article presents a review on different versions of the algorithm and the variousapplications. Then, a comprehensive study is made to reach highest acceleration rates, by comparing different ways to structure the information, using specialized instructions and parallel programming. The execution time is reduced significantly on different multi-core CPU architectures for embedded computing: Panda Board ARM Cortex 9 and an Intel Ultra Low voltage U9300. According to our experiments on Covariance Tracking (CT), it is possible to reach a speed-up of 3.75 on ARM Cortex and 5 on Intel, when compared to the original algorithm