  Vehicle-to-Pedestrian (V2P) communication can significantly improve
pedestrian safety at a signalized intersection. It is unlikely that pedestrians
will carry a low latency communication enabled device and activate a pedestrian
safety application in their hand-held device all the time. Because of this
limitation, multiple traffic cameras at the signalized intersection can be used
to accurately detect and locate pedestrians using deep learning and broadcast
safety alerts related to pedestrians to warn connected and automated vehicles
around a signalized intersection. However, unavailability of high-performance
computing infrastructure at the roadside and limited network bandwidth between
traffic cameras and the computing infrastructure limits the ability of
real-time data streaming and processing for pedestrian detection. In this
paper, we develop an edge computing based real-time pedestrian detection
strategy combining pedestrian detection algorithm using deep learning and an
efficient data communication approach to reduce bandwidth requirements while
maintaining a high object detection accuracy. We utilize a lossy compression
technique on traffic camera data to determine the tradeoff between the
reduction of the communication bandwidth requirements and a defined object
detection accuracy. The performance of the pedestrian-detection strategy is
measured in terms of pedestrian classification accuracy with varying peak
signal-to-noise ratios. The analyses reveal that we detect pedestrians by
maintaining a defined detection accuracy with a peak signal-to-noise ratio
(PSNR) 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to
0.31 Mbits/sec, a 31x reduction.
