The societal impact of the project is very high as it contributes to feasible, rapid, and affordable improvements in roadside safety in large urban areas. With the planned immediate deployment of the technology that leverages and improves existing infrastructure (i.e. gantry mounts), our solution also offers high economic benefits. The three main socially relevant aspects are (1) detection and identification of abnormal pedestrian and bicyclist events (i.e. change in motion direction and velocity) along public roads in real-time, regardless of weather conditions (e.g. clouds, Sun, rain) or time of day (e.g. dusk, dawn, or night); (2) already known and monitored 'hot spots' can now be augmented by the solution, which preserves the privacy of the detected and tracked individuals yet reliably detects their abnormal motions; (3) only salient features of the public road visual scene are extracted while preserving privacy (i.e. no identity reconstruction from DVS events) so that only relevant cues are used in deciding whether the scene is normal or abnormal. Using the BrainChip Akida neuromorphic platform to process event-based vision data on-site reduces system costs by a factor of 20 compared to conventional centralized GPU processing. For a quantitative comparison, in large quantities: the event-based camera costs $50, and the custom neuromorphic board developed by BrainChip with RaspberryPI costs $300; compared to GPU cost and a price of $1000 per COTS cameras, typically focusing on wider operating temperatures, high resolution, autofocus, vibration correction, improved optics, and dynamic ranges with or without additional light sources. A BrainChip Akida system with a DVS event camera consumes at most 7.6 W when processing camera data on-site. Conventional real-time vision processing systems (connected sometimes also with centralized data centers) require specialized GPUs operating at around 400 W per COTS5camera. Hence, the neuromorphic design enables at least a 100-fold power reduction; per 100 million expected devices, this continuously saves 40GW. This quantitative estimate assumes units × (400 W– 4 W) = 39.6 GW. In addition, on-site processing reduces transmitted data from about 30 MB s−1 in conventional systems to 300 B s−1, a 10 000-fold reduction. For this calculation, we assume 25 bytes per object (8 for timestamp, 16 for camera coordinates, 1 for class), and 10–12 objects are detected per second: 25 × 12 = 300 B. Event cameras detect changes around 33 times faster than conventional cameras and can be used at night and in poor weather conditions due to their wide dynamic range of up to 120 dB.
(20min delay)