EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

doi:doi:10.57702/nfqlu5a5

EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs.

BibTex:

@dataset{Liang_Wang_and_Nan_Zhang_and_Xiaoyang_Qu_and_Jianzong_Wang_and_Jiguang_Wan_and_Guokuan_Li_and_Kaiyu_Hu_and_Guilin_Jiang_and_Jing_Xiao_2024,
    abstract = {Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs.},
    author = {Liang Wang and Nan Zhang and Xiaoyang Qu and Jianzong Wang and Jiguang Wan and Guokuan Li and Kaiyu Hu and Guilin Jiang and Jing Xiao},
    doi = {10.57702/nfqlu5a5},
    institution = {No Organization},
    keyword = {'domain shift', 'edge computing', 'label distribution shift', 'video analytics'},
    month = {dec},
    publisher = {TIB},
    title = {EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices},
    url = {https://service.tib.eu/ldmservice/dataset/edgema--model-adaptation-system-for-real-time-video-analytics-on-edge-devices},
    year = {2024}
}