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British Journal of Photography
Weird Science - Issue 7846

‘Esther Hovers’ - Portfolio Section
Text by Sophie Wright, April 2016

Standing still, fast movements, repeatedly looking back over your shoulders - these are three of eight ‘anomalies’ classified as deviant behaviour by surveillance cameras. Though normally used to recognise and prevent criminal activity, Esther Hovers, a recent graduate from the Royal Academy of Art in the Hague, explores these technologies to question what constitutes normal behaviour and examine our relationship with public security. Hovers’ graduation project False Positives (a term used to describe an unfounded alert based on visuals gathered by the surveillance camera), arose from her fascination with the relationship between people and public spaces. The idea came while doing an internship in Paris; she was struck by the rigid organisation and heavy surveillance presence in the business district, La Défense. “Everyone wears suits and has a strict dress code and way of moving. It wasn’t only the architecture, but also the way people seem to behave there,” she comments. Having researched surveillance systems, she approached several experts, interested in the combination of technology and psychology. During these interviews, she was told about the eight anomalies that formed the basis of her work.

The crux was devising a way to visualise a complex idea, usually discussed in dense PhD publications, without focusing too much on the cameras themselves. Hovers decided to shoot in the financial an political district of Brussels, not just because of its symbolic representation of Europe, but also because of its different perspectives. Appropriating the visual language of the surveillance camera and its elevated position, she started to look for examples of the eight anomalies from different viewpoints. After trying out several approaches using single images, Hovers decided to layer the photographs to create montages where several examples of the anomalies could be spotted.

The layering of photographs almost echoes the way the anomalies were created. Through a process dubbed ‘machine learning’, the surveillance system is fed a multitude of examples so it can develop a pattern of normal and deviant behaviour. “After a few minutes you can see a pattern, which is really important in the algorithm,” explains Hovers. By recording anomalies that she saw and sometimes asking people to step into position, the montages gather several examples of body language that is classed as deviant.
The project is drawn together in a publication made up of photographs, drawings and text. Keen to provide viewers with context, each anomaly is introduced with a simple drawing in black pen on graph paper, which Hovers says illustrates “how structured this way of thinking is”. The photographs invite viewers to question on what is or isn’t ‘normal’ behaviour.