Evidence map›Paper›PMID 32153357›Full record

ArticleFrontiers in neuroscience2020

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

Bharath Ramesh, Andrés Ussa, Luca Della Vedova, Hong Yang, Garrick Orchard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Bharath RameshLife Science Institute, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.
Andrés UssaLife Science Institute, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.
Luca Della VedovaTemasek Laboratories, National University of Singapore, Singapore, Singapore.
Hong YangTemasek Laboratories, National University of Singapore, Singapore, Singapore.
Garrick OrchardLife Science Institute, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

closed-loop controlevent-based descriptorFIFO processinglow-power FPGAneuromorphic visionobject detectionobject recognitionrectangular grid

Identifiers

PMID32153357
PMCPMC7044237

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.