  As camera quality improves and their deployment moves to areas with limited
bandwidth, communication bottlenecks can impair real-time constraints of an ITS
application, such as video-based real-time pedestrian detection. Video
compression reduces the bandwidth requirement to transmit the video but
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 (e.g., rain
and darkness) alter the compression ratio and can make maintaining a high
pedestrian detection accuracy more difficult. 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 in order 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 selects the error tolerances dynamically
for lossy compression that can maintain a high detection accuracy across a
representative set of environmental conditions. Analyses reveal that our
strategy increases pedestrian detection accuracy up to 14% and reduces the
communication bandwidth up to 14x for adverse environmental conditions compared
to the same conditions but without our dynamic EBLC strategy. Our dynamic EBLC
strategy is independent of detection models and environmental conditions
allowing other detection models and environmental conditions to be easily
incorporated in our strategy.
