The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.