Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras

Frontiers in Neuroscience
Bharath RameshGarrick Orchard

Abstract

We present the first purely event-based, energy-efficient approach for dynamic object detection and categorization with a freely moving event camera. Compared to traditional cameras, event-based object recognition systems are considerably behind in terms of accuracy and algorithmic maturity. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional object representation when hardware resources are limited to implement PCA. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance compared to state-of-the-art algorithms. Additionally, we verified the real-time FPGA per...Continue Reading

References

Oct 12, 2013·Frontiers in Neuroscience·Peter O'ConnorMichael Pfeiffer
Dec 7, 2013·Frontiers in Neuroscience·Tobi Delbruck, Manuel Lang
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Garrick OrchardRyad Benosman
Dec 5, 2015·Frontiers in Neuroscience·Garrick OrchardNitish Thakor
Jun 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shaoqing RenJian Sun
Jul 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Xavier LagorceRyad B Benosman
Nov 24, 2016·Frontiers in Neuroscience·Jun Haeng LeeMichael Pfeiffer
May 31, 2019·IEEE Transactions on Pattern Analysis and Machine Intelligence·Bharath RameshCheng Xiang

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

VHDL
SmartFusion
MATLAB
SPM
SOD
DAVIS
RECT
Zynq
KDSEARCHFAST
ISIM

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