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

TL;DR

EBBIOT introduces a low-memory, low-computation object tracking method for IoVT using stationary neuromorphic sensors, combining event-based images with simple tracking to outperform traditional approaches.

Contribution

The paper proposes a novel mixed event/frame-based tracking paradigm with efficient noise filtering and occlusion handling, significantly reducing memory and computation.

Findings

Achieves >1000X less memory and computations than frame-based methods.

Outperforms EBMS and Kalman Filter in precision and recall on traffic data.

Requires 7X less memory and 3X less computation overall.

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.