Citation

Chen G, Cao H, Ye C, Zhang Z, Liu X, Mo X, Qu Z, Conradt J, Röhrbein F, Knoll A. Front. Neurorobotics 2019; 13: e10.

Affiliation

Robotics, Artificial Intelligence and Real-time Systems, Technische Universität München, München, Germany.

Copyright

(Copyright © 2019, Frontiers Research Foundation)

DOI

10.3389/fnbot.2019.00010

PMID

31001104

PMCID

PMC6454154

Abstract

Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.

Language: en

Keywords

convolutional neural network; event-stream encoding; multi-Cue event information fusion; neuromorphic vision sensor; object detection