Multi-view distributed coding and selection of local binary features
Recently, the latest advances in compact feature representation and feature learning have provided an efficient framework for several visual analysis tasks, such as object recognition. However, when multiple cameras with overlapping fields-of-view are employed, other visual analysis tasks such as depth estimation can be supported and object recognition accuracy can be improved. In this paper the problem of distributed visual analysis from multiple views of a scene is addressed, considering that computational power and bandwidth, at each camera sensor, are rather limited. More specifically, an efficient coding technique for local binary features is proposed which exploits the correlation at the decoder side between each descriptor and its quantized representation. Moreover, considering that descriptors representing the same visual feature across different views are well correlated, a technique to avoid the transmission of redundant descriptors from multiple views is proposed. At the decoder, the joint statistics of all descriptors from all views is used to drive the selection of the best descriptors to be transmitted by each sensing node. The proposed multi-view feature coding and selection techniques allow obtaining bitrate reductions up to 80%, with respect to the uncompressed descriptor rate, for a certain task accuracy.