EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors

Abstract

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.

HyNNA: Improved Performance for Neuromorphic Vision Sensor Based Surveillance using Hybrid Neural Network Architecture

“…There are two types of paradigms that can be used to find salient information from events based on: individual events or groups of events. A single event does not provide much information for classification problems, hence most research works explore a hybrid approach where groups of events are accumulated into a frame either using a fixed number of events [16], [17] or a fixed interval [18]. These frames are passed through a deep learning framework during training.…”