Visual Search algorithms are a class of methods that retrieve images by their content. In particular, given a database of reference images and a query image the goal is to find an image among the database that depicts the same object as in the query, if any. Moreover, in many different real case applications more than one object of interest could be viewed in the query image. Furthermore, in this kind of situations, often, it is not sufficient to identify the object depicted on a query image but its precise localization inside the scene viewed by the camera is also requested. In this paper we propose to couple a Visual Search system, which can retrieve multiple objects from the same query image, with an additional Distance Estimation module that exploits the localization information already computed inside the Visual Search stage to estimate localization of the object in three dimensions. In this work we implement the complete image retrieval and spatial localization pipeline (including relative distance estimation) on two different embedded devices, exploiting also their GPU in order to get near real time performances on low-power devices. Lastly, the accuracy of the proposed distance estimation is evaluated on a dataset of annotated query-reference pairs ad-hoc created.
|Titolo:||Embedded Real-Time Visual Search with Visual Distance Estimation|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|
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