Efficient Heavy Hitters Identification Over Speed Traffic Streams

Zhang, Shuzhuang; Luo, Hao; Wu, Zhigang; Wang, Yanbin Sun Yuhang; Yuan, Tingting

doi:10.32604/cmc.2020.07496

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Apart from the introduction of small traffic alerts, similar work could be applied in several other promising scenarios, including Blockchain (Zhang et al 2020b), Big Data (Wang et al 2020), or other IoT systems (Chen et al 2019a;Penna and Orefice 2019;Kabir et al 2019). Zhang et al (2020c) (5)…”

Section: Densenetmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

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An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.

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Section: Densenetmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…The 5G mobile systems can monitor network conditions and learn with network data with the machine learning algorithms. In [12], the authors use the EBF sketches method to combine Bloom Filter with an exponential histogram to query streams in the sliding window to identify heavy vehicles in the speed traffic streams. In [13], the authors propose an analysis method based on the priority of the wireless sensor networks (WSNs) in Highway Traffic to get the accurate location of the node.…”

Section: Introductionmentioning

confidence: 99%