Edge Computing-Enabled Crowd Density Estimation based on Lightweight Convolutional Neural Network

Wang, Shuo; Pu, Ziyuan; Li, Qianmu; Guo, Yaming; Li, Meng

doi:10.1109/isc253183.2021.9562877

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…RMSE: Nearly 0.035% Wang et al [104] Improve operational efficiency considerably with the same-level accuracy.…”

Section: Acceleration: Nearly 2xmentioning

confidence: 99%

“…Deep learning technologies combined with edge computing also play an important role in public safety. Wang et al [104] proposed a lightweight model based on the residual bottleneck block and dilated convolutional for crowd density estimation so that once accidents take place, feasible and efficient evacuation strategies could be developed in time. Compared with the state-of-the-art models, this model could compress nearly half of the parameters without much loss of accuracy.…”

Section: Monitoring and Predictionmentioning

confidence: 99%

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Edge Intelligence with Distributed Processing of DNNs: A Survey

Tang¹,

Cui²,

Qi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models' training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices, which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing. Compared with existing papers, this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing. Considering the practicalities, commonly used lightweight models in a distributed system are introduced as well. As the key technique, the parallel strategy will be described in detail. Then some typical applications of distributed processing will be analyzed. Finally, the challenges of distributed processing with edge computing will be described.

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“…RMSE: Nearly 0.035% Wang et al [104] Improve operational efficiency considerably with the same-level accuracy.…”

Section: Acceleration: Nearly 2xmentioning

confidence: 99%

Section: Monitoring and Predictionmentioning

confidence: 99%

Edge Intelligence with Distributed Processing of DNNs: A Survey

Tang¹,

Cui²,

Qi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Through a multi-task learning method, the scale variation problem was solved and crowd counting was achieved accurately. Through applying an edge computing method, Wang et al [22] achieved crowd density estimation efficiently to solve the problem of high network latency caused by the deployment of the density estimation platform in the server; Zhang et al [23] proposed the adaptive multi-scale context aggregation network (MSCANet) to obtain the full-scale information of the crowd. After the information of different scales was extracted by the network, this information was fused by the network adaptively, which was suitable for crowd counting.…”

Section: Related Workmentioning

confidence: 99%

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

Wang

Cheng

Wang

et al. 2022

Sensors

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At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.

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