Related papers: Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision

Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision

URL: http://arxiv.org/abs/2410.00368v1
Date: Tue, 1 Oct 2024 03:42:03 GMT
Title: Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision
Authors: Riadul Islam, Sri Ranga Sai Krishna Tummala, Joey Mulé, Rohith Kankipati, Suraj Jalapally, Dhandeep Challagundla, Chad Howard, Ryan Robucci,
Abstract summary: This dataset is an annotated, temporal-threshold-based vision dataset for face detection tasks derived from the same videos used for Aff-Wild2. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds.
Score: 0.8271394038014485
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}.

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