  Deep Neural Networks (DNNs) can achieve state-of-the-art accuracy in many
computer vision tasks, such as object counting. Object counting takes two
inputs: an image and an object query and reports the number of occurrences of
the queried object. To achieve high accuracy on such tasks, DNNs require
billions of operations, making them difficult to deploy on
resource-constrained, low-power devices. Prior work shows that a significant
number of DNN operations are redundant and can be eliminated without affecting
the accuracy. To reduce these redundancies, we propose a hierarchical DNN
architecture for object counting. This architecture uses a Region Proposal
Network (RPN) to propose regions-of-interest (RoIs) that may contain the
queried objects. A hierarchical classifier then efficiently finds the RoIs that
actually contain the queried objects. The hierarchy contains groups of visually
similar object categories. Small DNNs are used at each node of the hierarchy to
classify between these groups. The RoIs are incrementally processed by the
hierarchical classifier. If the object in an RoI is in the same group as the
queried object, then the next DNN in the hierarchy processes the RoI further;
otherwise, the RoI is discarded. By using a few small DNNs to process each
image, this method reduces the memory requirement, inference time, energy
consumption, and number of operations with negligible accuracy loss when
compared with the existing object counters.
