Modeling local descriptors with multivariate gaussians for object and scene recognition
Proceedings of the 21st ACM international conference on Multimedia
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In Computer Vision and Pattern Recognition, the object detection problem is a fundamental task, but only a few systems are thought to be realized on an embedded architecture. To this end, we propose an effective, low-latency, affordable classification architecture, especially suited for embedded platforms. In particular, we have designed a novel highly-parallelizable classification framework for an FPGA-based implementation, which is suitable for generic detection problems. The underlying model consists in a weighted sum of boosted binary classifiers, learned on a set of overlapped image patches. Each patch is described by estimating the covariance matrix of a set of features, so forming a very compact and expressive descriptor. Covariances matrices live on Riemannian Manifold, whose topology is particularly simple, so that they can be approximated in the Euclidean Vector Space in a cheap and conservative way. The hardware design has been developed in a parallel fashion and with specific architectural solutions, allowing a fast response without degrading the functional performances. We finally specialize this architecture to the challenging pedestrian detection problem, defining state-of-the art results on the standard INRIA pedestrian benchmark dataset.