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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15628 |
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| _version_ | 1866912494738472960 |
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| author | Tang, Shanjiang Huang, Rui Luo, Hsinyu Wang, Chunjiang Yu, Ce Li, Yusen Fu, Hao Sun, Chao Xiao, and Jian |
| author_facet | Tang, Shanjiang Huang, Rui Luo, Hsinyu Wang, Chunjiang Yu, Ce Li, Yusen Fu, Hao Sun, Chao Xiao, and Jian |
| contents | The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications Tang, Shanjiang Huang, Rui Luo, Hsinyu Wang, Chunjiang Yu, Ce Li, Yusen Fu, Hao Sun, Chao Xiao, and Jian Computer Vision and Pattern Recognition The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics. |
| title | A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.15628 |