EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
This addresses efficient object tracking for surveillance in resource-constrained IoVT environments, representing an incremental improvement over existing event-based methods.
The paper tackles object tracking in low-power Internet of Video Things (IoVT) nodes by proposing EBBIOT, a mixed event-based and frame-based algorithm that uses event-based binary images for efficient noise filtering and region proposals, resulting in >1000X less memory and computes than frame-based methods, 7X less memory and 3X less computations than conventional approaches, and higher precision and recall in traffic recordings.
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT). Different from fully event based tracking or fully frame based approaches, we propose a mixed approach where we create event-based binary images (EBBI) that can use memory efficient noise filtering algorithms. We exploit the motion triggering aspect of neuromorphic sensors to generate region proposals based on event density counts with >1000X less memory and computes compared to frame based approaches. We also propose a simple overlap based tracker (OT) with prediction based handling of occlusion. Our overall approach requires 7X less memory and 3X less computations than conventional noise filtering and event based mean shift (EBMS) tracking. Finally, we show that our approach results in significantly higher precision and recall compared to EBMS approach as well as Kalman Filter tracker when evaluated over 1.1 hours of traffic recordings at two different locations.