Low-Power Object Counting with Hierarchical Neural Networks

by   Abhinav Goel, et al.

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.


page 1

page 2

page 6


Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks

Low-power computer vision on embedded devices has many applications. Thi...

A Survey of Methods for Low-Power Deep Learning and Computer Vision

Deep neural networks (DNNs) are successful in many computer vision tasks...

Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks

Computer vision on low-power edge devices enables applications including...

On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision

Processing visual data on mobile devices has many applications, e.g., em...

Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

Deep neural networks (DNNs) have been expanded into medical fields and t...

An Analysis of Deep Neural Network Models for Practical Applications

Since the emergence of Deep Neural Networks (DNNs) as a prominent techni...

Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

Nanopore genome sequencing is the key to enabling personalized medicine,...

Please sign up or login with your details

Forgot password? Click here to reset