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

Abstract

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.

Real Time Object Detection Can be Embedded on Low Powered Devices

“…It is also unlikely that pedestrians will always carry a pedestrian safety hand held device with them and get signals through the help of a low latency communication device. We can improve this model as developed by Rahman et al [12] where they studied the model with the help of traffic camera to precisely pinpoint the location of pedestrians using deep learning to broadcast safety alerts related to pedestrians to nearby vehicles around signalized intersections. They successfully build an efficient real time pedestrian detection strategy combining pedestrian algorithm using deep learning while maintaining high object detection accuracy.…”

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

“…Besides these sensors, surveillance cameras provide researchers and city planners with real scenes of traffic conditions. With traffic cameras, it is possible to not only extract vehicle count, density, and speed, but also vehicle motion patterns, and behavior and interaction between vehicle and pedestrians (14)(15)(16)(17)(18)(19)(20)(21)(22). With comprehensive and rich content, surveillance videos can provide the task of traffic control with detailed and accurate traffic information.…”