Compressive sensing based video object compression schemes for surveillance systems
In some surveillance videos, successive frames exhibit correlation in the sense that only a small portion changes (object motion). If the foreground moving objects are segmented from the background they can be coded independently requiring far fewer bits compared to frame-based coding. Huang et al p...
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| Format: | Conference Paper |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/88460 http://hdl.handle.net/10220/46929 |
| _version_ | 1826122797508722688 |
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| author | Narayanan, Sathiya Makur, Anamitra |
| author2 | Loce, Robert P. |
| author_facet | Loce, Robert P. Narayanan, Sathiya Makur, Anamitra |
| author_sort | Narayanan, Sathiya |
| collection | NTU |
| description | In some surveillance videos, successive frames exhibit correlation in the sense that only a small portion changes (object motion). If the foreground moving objects are segmented from the background they can be coded independently requiring far fewer bits compared to frame-based coding. Huang et al proposed a Compressive Sensing (CS) based Video Object Error Coding (CS-VOEC) where the objects are segmented and coded via motion estimation and compensation. Since motion estimation might be computationally intensive, encoder can be kept simple by performing motion estimation the decoder rather than at the encoder. We propose a novel CS based Video Object Compression (CS-VOC) technique having a simple encoder in which the sensing mechanism is applied directly on the segmented moving objects using a CS matrix. At the decoder, the object motion is first estimated so that a CS reconstruction algorithm can efficiently recover the sparse motion-compensated video object error. In addition to simple encoding, simulation results show our coding scheme performs on par with the state-of-the-art CS based video object error coding scheme. If the object segmentation requires more computations, we propose to deploy a distributed CS framework called Distributed Compressive Video Sensing based Video Object Compression (DCVS-VOC) wherein the object segmentation is done only for key frames. |
| first_indexed | 2024-10-01T05:54:23Z |
| format | Conference Paper |
| id | ntu-10356/88460 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:54:23Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/884602020-03-07T13:24:45Z Compressive sensing based video object compression schemes for surveillance systems Narayanan, Sathiya Makur, Anamitra Loce, Robert P. Saber, Eli School of Electrical and Electronic Engineering Proceedings of SPIE - Video Surveillance and Transportation Imaging Applications 2015 Video Object Coding Distributed Compressive Sensing DRNTU::Engineering::Electrical and electronic engineering In some surveillance videos, successive frames exhibit correlation in the sense that only a small portion changes (object motion). If the foreground moving objects are segmented from the background they can be coded independently requiring far fewer bits compared to frame-based coding. Huang et al proposed a Compressive Sensing (CS) based Video Object Error Coding (CS-VOEC) where the objects are segmented and coded via motion estimation and compensation. Since motion estimation might be computationally intensive, encoder can be kept simple by performing motion estimation the decoder rather than at the encoder. We propose a novel CS based Video Object Compression (CS-VOC) technique having a simple encoder in which the sensing mechanism is applied directly on the segmented moving objects using a CS matrix. At the decoder, the object motion is first estimated so that a CS reconstruction algorithm can efficiently recover the sparse motion-compensated video object error. In addition to simple encoding, simulation results show our coding scheme performs on par with the state-of-the-art CS based video object error coding scheme. If the object segmentation requires more computations, we propose to deploy a distributed CS framework called Distributed Compressive Video Sensing based Video Object Compression (DCVS-VOC) wherein the object segmentation is done only for key frames. Published version 2018-12-12T08:56:49Z 2019-12-06T17:03:47Z 2018-12-12T08:56:49Z 2019-12-06T17:03:47Z 2015 Conference Paper Narayanan, S., & Makur, A. (2015). Compressive sensing based video object compression schemes for surveillance systems. Proceedings of SPIE - Video Surveillance and Transportation Imaging Applications 2015, 9407, 94070W-. doi:10.1117/12.2081806 https://hdl.handle.net/10356/88460 http://hdl.handle.net/10220/46929 10.1117/12.2081806 en © 2015 Society of Photo-optical Instrumentation Engineers (SPIE). This paper was published in Proceedings of SPIE - Video Surveillance and Transportation Imaging Applications 2015 and is made available as an electronic reprint (preprint) with permission of Society of Photo-optical Instrumentation Engineers (SPIE). The published version is available at: [http://dx.doi.org/10.1117/12.2081806]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 7 p. application/pdf |
| spellingShingle | Video Object Coding Distributed Compressive Sensing DRNTU::Engineering::Electrical and electronic engineering Narayanan, Sathiya Makur, Anamitra Compressive sensing based video object compression schemes for surveillance systems |
| title | Compressive sensing based video object compression schemes for surveillance systems |
| title_full | Compressive sensing based video object compression schemes for surveillance systems |
| title_fullStr | Compressive sensing based video object compression schemes for surveillance systems |
| title_full_unstemmed | Compressive sensing based video object compression schemes for surveillance systems |
| title_short | Compressive sensing based video object compression schemes for surveillance systems |
| title_sort | compressive sensing based video object compression schemes for surveillance systems |
| topic | Video Object Coding Distributed Compressive Sensing DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/88460 http://hdl.handle.net/10220/46929 |
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