  General object detectors use powerful backbones that uniformly extract
features from images for enabling detection of a vast amount of object types.
However, utilization of such backbones in object detection applications
developed for specific object types can unnecessarily over-process an extensive
amount of background. In addition, they are agnostic to object scales, thus
redundantly process all image regions at the same resolution. In this work we
introduce BLT-net, a new low-computation two-stage object detection
architecture designed to process images with a significant amount of background
and objects of variate scales. BLT-net reduces computations by separating
objects from background using a very lite first-stage. BLT-net then efficiently
merges obtained proposals to further decrease processed background and then
dynamically reduces their resolution to minimize computations. Resulting image
proposals are then processed in the second-stage by a highly accurate model. We
demonstrate our architecture on the pedestrian detection problem, where objects
are of different sizes, images are of high resolution and object detection is
required to run in real-time. We show that our design reduces computations by a
factor of x4-x7 on the Citypersons and Caltech datasets with respect to leading
pedestrian detectors, on account of a small accuracy degradation. This method
can be applied on other object detection applications in scenes with a
considerable amount of background and variate object sizes to reduce
computations.
