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Vehicle-to-Pedestrian (V2P) communication can significantly improvepedestrian safety at a signalized intersection. It is unlikely that pedestrianswill carry a low latency communication enabled device and activate a pedestriansafety application in their hand-held device all the time. Because of thislimitation, multiple traffic cameras at the signalized intersection can be usedto accurately detect and locate pedestrians using deep learning and broadcastsafety alerts related to pedestrians to warn connected and automated vehiclesaround a signalized intersection. However, unavailability of high-performancecomputing infrastructure at the roadside and limited network bandwidth betweentraffic cameras and the computing infrastructure limits the ability ofreal-time data streaming and processing for pedestrian detection. In thispaper, we develop an edge computing based real-time pedestrian detectionstrategy combining pedestrian detection algorithm using deep learning and anefficient data communication approach to reduce bandwidth requirements whilemaintaining a high object detection accuracy. We utilize a lossy compressiontechnique on traffic camera data to determine the tradeoff between thereduction of the communication bandwidth requirements and a defined objectdetection accuracy. The performance of the pedestrian-detection strategy ismeasured in terms of pedestrian classification accuracy with varying peaksignal-to-noise ratios. The analyses reveal that we detect pedestrians bymaintaining a defined detection accuracy with a peak signal-to-noise ratio(PSNR) 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to0.31 Mbits/sec, a 31x reduction.

Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy