1. A method for learning a plurality of view-specific object detectors as a function of scene geometry and object motion patterns, the method comprising: determining via a processing unit motion directions for each of a plurality of object images that are extracted from a source training video dataset input and that each have size and motion dimension values that meet an expected criterion of an object of interest, wherein the object images are collected from each of a plurality of different camera scene viewpoints; categorizing via the processing unit the plurality of object images into a plurality of clusters as a function of similarities of their determined motion directions, wherein the object images in each of the clusters are also acquired from one of the different camera scene viewpoints; estimating via the processing unit zenith angles for poses of the object images in each of the clusters relative to a position of a horizon in the camera scene viewpoint from which the clustered object images are acquired, and azimuth angles of the poses as a function of a relation of the determined motion directions of the clustered object images to the camera scene viewpoint from which the clustered object images are acquired; and building via the processing unit a plurality of detectors for recognizing objects input video, one for each of the clusters of the object images, and associating each of the built detectors with the estimated zenith angles and azimuth angles of the poses of the cluster for which the detectors are built.