Face Detection Dataset for Programmable Threshold-Based Sparse-Vision

Citation Author(s):
Riadul
Islam
University of Maryland Baltimore County
Submitted by:
Riadul Islam
Last updated:
Mon, 09/30/2024 - 17:01
DOI:
10.21227/bw2e-dj78
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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, producevarious 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://github.com/riaduli/Thresholded_event_vision_face_dataset}.

Instructions: 

all_threshold_15fps.zip

 

threshold_4.zip

–test: 904 .txt and 904 .png files

–train: 6392 .txt and 904 .png files

–valid: 1834 .txt and 1834 .png files

 

threshold_8.zip

–test: 904 .txt and 904 .png files

–train: 6392 .txt and 904 .png files

–valid: 1834 .txt and 1834 .png files

 

threshold_12.zip

–test: 904 .txt and 904 .png files

–train: 6392 .txt and 904 .png files

–valid: 1834 .txt and 1834 .png files

 

threshold_16.zip

–test: 904 .txt and 904 .png files

–train: 6392 .txt and 904 .png files

–valid: 1834 .txt and 1834 .png files

 

Funding Agency: 
National Science Foun- dation (NSF) under award number 2138253, the Maryland Industrial Partnerships (MIPS) program under award number MIPS0012, and the UMBC Startup grant.