Oxford Town Centre dataset

Oxford Town Centre is a dataset created by researchers at Oxford University for the research and development of pedestrian activity and facial recognition systems. 

The data was collected in 2009 from a public safety CCTV camera in the middle of Oxford and captured the movements of approximately 2,000 people.

The dataset proved popular, having been used in over 60 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. 

It has been downloaded over 700 times on Kaggle.

Risks and harms 🛑

The Oxford Town Centre dataset raises privacy concerns due to its collection of video footage of pedestrians in public spaces without consent, potentially enabling the tracking and analysis of individuals' movements and behaviours without their knowledge or permission. 

Transparency and accountability 🙈

The Oxford Town Centre dataset has several transparency limitations:

• Lack of consent. The dataset uses footage from a public surveillance camera, capturing images of pedestrians without their knowledge or consent. This raises significant ethical concerns regarding privacy and informed participation.

• Data collection. While it is known that the dataset consists of a 5-minute video with 7,500 annotated frames, the full extent of the data collection, curation, and annotation process is not clear.

• Subject demographics. There appears to be a lack of comprehensive information about the subjects' demographics, making it difficult to assess potential biases or representativeness of the data.

• Usage guidelines. There is a lack of clear guidelines or restrictions on how the dataset can be used, potentially leading to misuse or unethical applications of the surveillance footage.

• Data retention and access policies. Information about how long the data is retained, who has access to it, and how it is protected from unauthorised use is not readily apparent.

Investigations, assessments, audits 🧐

• Harvey, A., LaPlace, J. (2019). Exposing.ai