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

Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query image against a gallery of previously seen images. State-of-the-art techniques rely on large, computationally-intensive De...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Goel, Abhinav, Tung, Caleb, Hu, Xiao, Wang, Haobo, Davis, James C, Thiruvathukal, George K, Lu, Yung-Hsiang
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
Electronic devices
Energy consumption
Network latency
Neural networks
Power management
Queries
Query processing
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Summary:Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query image against a gallery of previously seen images. State-of-the-art techniques rely on large, computationally-intensive Deep Neural Networks (DNNs). We propose a novel hierarchical DNN architecture that uses attribute labels in the training dataset to perform efficient object reID. At each node in the hierarchy, a small DNN identifies a different attribute of the query image. The small DNN at each leaf node is specialized to re-identify a subset of the gallery: only the images with the attributes identified along the path from the root to a leaf. Thus, a query image is re-identified accurately after processing with a few small DNNs. We compare our method with state-of-the-art object reID techniques. With a 4% loss in accuracy, our approach realizes significant resource savings: 74% less memory, 72% fewer operations, and 67% lower query latency, yielding 65% less energy consumption.
ISSN:2331-8422