PixelSieve: Towards Efficient Activity Analysis From Compressed Video Streams
Pixel-level data redundancy in video induces additional memory and computing overhead when neural networks are employed to mine spatiotemporal patterns, e.g. activity and event labels from video streams. This work proposes PixelSieve, to enable highly efficient CNN-based activity analysis directly f...
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| Vydáno v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 811 - 816 |
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05.12.2021
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| Abstract | Pixel-level data redundancy in video induces additional memory and computing overhead when neural networks are employed to mine spatiotemporal patterns, e.g. activity and event labels from video streams. This work proposes PixelSieve, to enable highly efficient CNN-based activity analysis directly from video data in compressed formats. Instead of recovering original RGB frames from compressed video, PixelSieve utilizes the built-in metadata in compressed video streams to distill only the critical pixels that render relevant spatiotemporal features, and then conducts efficient CNN inference with the condensed inputs. PixelSieve removes the overhead of video decoding and significantly improves the performance of CNN-based video analysis by 4.5x on average. |
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| AbstractList | Pixel-level data redundancy in video induces additional memory and computing overhead when neural networks are employed to mine spatiotemporal patterns, e.g. activity and event labels from video streams. This work proposes PixelSieve, to enable highly efficient CNN-based activity analysis directly from video data in compressed formats. Instead of recovering original RGB frames from compressed video, PixelSieve utilizes the built-in metadata in compressed video streams to distill only the critical pixels that render relevant spatiotemporal features, and then conducts efficient CNN inference with the condensed inputs. PixelSieve removes the overhead of video decoding and significantly improves the performance of CNN-based video analysis by 4.5x on average. |
| Author | Wang, Ying Wang, Yongchen Li, Xiaowei Li, Huawei |
| Author_xml | – sequence: 1 givenname: Yongchen surname: Wang fullname: Wang, Yongchen email: wangyongchen@ict.ac.cn organization: SKLCA, Institute of Computing Technology,Chinese Academy of Sciences – sequence: 2 givenname: Ying surname: Wang fullname: Wang, Ying email: wangying2009@ict.ac.cn organization: SKLCA, Institute of Computing Technology,Chinese Academy of Sciences – sequence: 3 givenname: Huawei surname: Li fullname: Li, Huawei email: lihuawei@ict.ac.cn organization: SKLCA, Institute of Computing Technology,Chinese Academy of Sciences – sequence: 4 givenname: Xiaowei surname: Li fullname: Li, Xiaowei email: lxw@ict.ac.cn organization: SKLCA, Institute of Computing Technology,Chinese Academy of Sciences |
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| Snippet | Pixel-level data redundancy in video induces additional memory and computing overhead when neural networks are employed to mine spatiotemporal patterns, e.g.... |
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| SubjectTerms | Decoding Design automation Metadata Neural networks Redundancy Spatiotemporal phenomena Streaming media |
| Title | PixelSieve: Towards Efficient Activity Analysis From Compressed Video Streams |
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