ETH Zurich students unknowingly included in pedestrian detection dataset
Occurred: June 2019
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A dataset created by Swiss researchers to detect and identify pedestrians was accused of privacy abuse and potentially enabling the development of military reconnaisance drones and other ethically dubious products.
École Polytechnique Fédérale de Lausanne (EPFL), ETH Zurich and Idiap researchers created the WILDTRACK dataset of thousands of students, faculty and others to-ing and fro-ing 'in the wild' outside the main building at ETH University, Zurich, with the aim of improving pedestrian and other surveillance systems.
However, the recordings were largely made without the knowledge or consent of those captured due to the size and inconspicuous nature of notices were placed underneath each of the cameras, according to exposing.ai researcher Adam Harvey. Furthermore, the students had almost no recourse for redaction as the data is shared, copied, and manipulated across multiple legal jurisdictions, Harvey noted.
WILDTRACK was made openly available for any type of research, with potential applications envisaged (pdf) by the researchers including security, surveillance, remote person identification, robotics, autonomous driving, and crowdsourcing. Published research studies reveal that WILDTRACK was used by academic and commercial entities such as the Nanjing University of Aeronautics (NUAA), the University of Leicester, Microsoft and Chinese retail group Wormplex to improve drone and retail surveillance.
Harvey notes that 'NUAA has produced over 40 unmanned aerial vehicles (UAVs) for China, most of which are small or micro sized UAVs with consumer or industrial surveillance capabilities ... [with] a limited number .. made specifically for military reconnaissance.'
Operator: École Polytechnique Fédérale de Lausanne (EPFL); ETH University; Microsoft; Nanjing University of Aeronautics and Astronautics (NUAA); Universidad Autónoma de Madrid; University of Leicester; Wormplex AI
Developer: École Polytechnique Fédérale de Lausanne; ETH University
Country: Switzerland
Sector: Technology; Research/academia
Purpose: Improve pedestrian detection
Technology: Database/dataset; Computer vision; Pattern recognition; Pedestrian detection;
Issue: Ethics/values; Dual/multi-use; Privacy; Surveillance
Transparency: Privacy
Investigations, assessments, audits 🧐
Harvey, A., LaPlace, J. (2019). Exposing.ai
Zhu H., Qi Y., Shi H., Li N., Zhou H. (2018). Human Detection Under UAV: an Improved Faster R-CNN Approach
Chakraborty I., Hua G. (2019). Priming Deep Pedestrian Detection with Geometric Context
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Published: July 2024