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Dynamic error-bounded lossy compression to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications

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Abstract

As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an intelligent transportation systems application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video which degrades the video quality. As the quality level of the video decreases, it results in the corresponding decreases in the accuracy of the vision-based pedestrian detection model. Furthermore, environmental conditions, such as rain and night-time darkness impact the ability to leverage compression by making it more difficult to maintain high pedestrian detection accuracy. The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions to maintain a high pedestrian detection accuracy. We conduct a case study to show the efficacy of our dynamic EBLC strategy for real-time vision-based pedestrian detection under adverse environmental conditions. Our strategy dynamically selects the lossy compression error tolerances that maintain a high detection accuracy across a representative set of environmental conditions. Analyses reveal that for adverse environmental conditions, our dynamic EBLC strategy increases pedestrian detection accuracy up to 14% and reduces the communication bandwidth up to 14 × compared to the state-of-the-practice. Moreover, we show our dynamic EBLC strategy is independent of pedestrian detection models and environmental conditions allowing other detection models and environmental conditions to be easily incorporated.

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Acknowledgements

This material is based on a study partially supported by the Center for Connected Multimodal Mobility (C2M2) (USDOT Tier 1 University Transportation Center) Grant headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof. This material is also based upon work supported by the National Science Foundation under Grant No. SHF-1910197.

Funding

This material is based on a study partially supported by the Center for Connected Multimodal Mobility (C2M2) (USDOT Tier 1 University Transportation Center) Grant headquartered at Clemson University, Clemson, South Carolina, USA. This material is also based upon work supported by the National Science Foundation under Grant No. SHF-1910197.

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Authors and Affiliations

  1. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA

    Mizanur Rahman

  2. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Mhafuzul Islam & Mashrur Chowdhury

  3. Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA

    Cavender Holt & Jon Calhoun

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  1. Mizanur Rahman
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  2. Mhafuzul Islam
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  3. Cavender Holt
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  4. Jon Calhoun
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  5. Mashrur Chowdhury
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Contributions

The authors confirm contribution to the paper as follows: MR: conceptualization; methodology; data curation; formal analysis; and roles/writing—original draft. MI: data curation; formal analysis; and writing—original draft preparation. CH: formal analysis and writing—original draft preparation. JC: conceptualization, funding acquisition; writing—review and editing. MC: conceptualization, methodology, funding acquisition; writing—review and editing.

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Correspondence to Mizanur Rahman.

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Rahman, M., Islam, M., Holt, C. et al. Dynamic error-bounded lossy compression to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications. J Real-Time Image Proc 19, 117–131 (2022). https://doi.org/10.1007/s11554-021-01165-0

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