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

• URL: http://arxiv.org/abs/2106.10588v1
• Date: Sat, 19 Jun 2021 23:59:26 GMT
• Title: Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks
• Authors: Abhinav Goel, Caleb Tung, Xiao Hu, Haobo Wang, James C. Davis, George K. Thiruvathukal, Yung-Hsiang Lu
• Abstract summary: 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. 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.
• Score: 9.884285377021044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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