Title: Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences,
Abstract: Video analytics requires operating with large amounts of data. Compressive
sensing allows to reduce the number of measurements required to represent the
video using the prior knowledge of sparsity of the original signal, but it
imposes certain conditions on the design matrix. The Bayesian compressive
sensing approach relaxes the limitations of the conventional approach using the
probabilistic reasoning and allows to include different prior knowledge about
the signal structure. This paper presents two Bayesian compressive sensing
methods for autonomous object detection in a video sequence from a static
camera. Their performance is compared on the real datasets with the
non-Bayesian greedy algorithm. It is shown that the Bayesian methods can
provide the same accuracy as the greedy algorithm but much faster; or if the
computational time is not critical they can provide more accurate results.