Vehicle detection and tracking method based on multi-sensor trajectory information

Zhao, Linfeng; Cao, Qinxing; Cai, Bixin; Shao, Wang; Zhang, Manling

doi:10.1007/s40430-022-03937-6

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Unlike traditional target-tracking algorithms, heterogeneoussensing-based target-tracking methods not only extend the normal operating life of sensors by scheduling various types of sensor nodes to shorten sensor operation time but also improve detection accuracy through multi-source heterogeneous sensing systems [19]. The data received by the sensors is no longer the only source of data: new technologies transform the information collected by various types of sensors into more valuable information [20].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Li²,

Chen³

et al. 2023

Meas. Sci. Technol.

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Target tracking applications are promising, it has great theoretical and practical significance, but the research faces great challenges. With the development of multi-modal depth sensing technology, a large number of scholars have proposed various target tracking methods based on heterogeneous sensing and got great results. This paper provides an overview of the techniques involved in target tracking in the different layers of the network and a comprehensive analysis of the research progress in heterogeneous sensing techniques in each layer. First, this paper introduced the single sensing scheme and heterogeneous sensing scheme in the physical layer. Second, we presented heterogeneous communication technologies and heterogeneous optimization methods for communication protocols in the network layer. Third, we combined several typical heterogeneous sensory target tracking applications and analyzed the application of cloud computing, edge computing, big data, and blockchain technologies. Finally, we discussed the challenges and future direction of heterogeneous sensory target tracking methods.

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Section: Introductionmentioning

confidence: 99%