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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.