Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification

The large-scale use of surveillance cameras in public spaces raised severe concerns about an individual privacy breach. Introducing privacy and security in video surveillance systems, primarily in person re-identification (re-id), is quite challenging. Event cameras are novel sensors, which only respond to brightness changes in the scene. This characteristic makes event-based vision sensors viable for privacy-preserving in video surveillance. Integrating privacy into the person re-id; this work investigates the possibility of performing person re-id with the event-camera network for the first time. We transform the asynchronous events stream generated by an event camera into synchronous image-like representations to leverage deep learning models and then evaluate how complex the re-id problem is with this new sensor modality. Interestingly, such event-based representations contain meaningful spatial details which are very similar to standard edges and contours. We use two different representations, image-like representation and their transformation to polar coordinates (which carry more distinct edge patterns). Finally, we train a person re-id model on such images to demonstrate the feasibility of performing event-driven re-id. We evaluate the performance of our approach and produce baseline results on two synthetic datasets (generated from publicly available datasets, SAIVT and DukeMTMC-reid).