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  • Chen, GuangTongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

  • Artikel/kapitelEngelska2018

Förlag, utgivningsår, omfång ...

  • Hindawi Limited,2018
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:kth-240793
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240793URI
  • https://doi.org/10.1155/2018/4815383DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • QC 20190103
  • Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur. This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Cao, HuHunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China. (författare)
  • Aafaque, MuhammadTech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)
  • Chen, JienengTongji Univ, Coll Elect & Informat Engn, Shanghai, Peoples R China. (författare)
  • Ye, CanboTongji Univ, Coll Automot Engn, Shanghai, Peoples R China. (författare)
  • Roehrbein, FlorianTech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)
  • Conradt, JörgKTH,Beräkningsvetenskap och beräkningsteknik (CST)(Swepub:kth)u1w41vp7 (författare)
  • Chen, KaiTongji Univ, Coll Automot Engn, Shanghai, Peoples R China. (författare)
  • Bing, ZhenshanTech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)
  • Liu, XingboTongji Univ, Coll Automot Engn, Shanghai, Peoples R China. (författare)
  • Hinz, GereonTech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)
  • Stechele, WalterTech Univ Munich, Integrated Syst, Munich, Germany. (författare)
  • Knoll, AloisTech Univ Munich, Robot & Embedded Syst, Munich, Germany. (författare)
  • Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Robot & Embedded Syst, Munich, Germany.Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China. (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Journal of Advanced Transportation: Hindawi Limited0197-67292042-3195

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