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Low-power computer vision on embedded devices has many applications. Thispaper 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 DeepNeural Networks (DNNs). We propose a novel hierarchical DNN architecture thatuses attribute labels in the training dataset to perform efficient object reID.At each node in the hierarchy, a small DNN identifies a different attribute ofthe query image. The small DNN at each leaf node is specialized to re-identifya subset of the gallery: only the images with the attributes identified alongthe path from the root to a leaf. Thus, a query image is re-identifiedaccurately after processing with a few small DNNs. We compare our method withstate-of-the-art object reID techniques. With a 4% loss in accuracy, ourapproach realizes significant resource savings: 74% less memory, 72% feweroperations, and 67% lower query latency, yielding 65% less energy consumption.

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