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

   https://doi.org/10.1177/0361198119843255  

   Rahman, Mizanur; Islam, Mhafuzul; Calhoun, Jon; Chowdhury, Mashrur (May 2019, Transportation Research Record: Journal of the Transportation Research Board)

   Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could 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 signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the 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 describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian 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 pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to 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 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction. 

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2. Elastic band based pedestrian collision avoidance using V2X communication

   https://doi.org/10.1109/IVS.2017.7995731  

   Gelbal, Sukru Yaren; Arslan, Sibel; Wang, Haoan; Aksun-Guvenc, Bilin; Guvenc, Levent (June 2017, 2017 IEEE Intelligent Vehicles Symposium (IV))

   This paper is on a pedestrian collision warning and avoidance system for road vehicles based on V2X communication. In cases where the presence and location of a pedestrian or group of pedestrians cannot be determined using line-of-sight sensors like camera, radar and lidar, signals from pedestrians' smartphone apps are used to detect and localize them relative to the road vehicle through the DSRC radio used for V2X communication. A hardware-in-the-loop setup using a validated automated driving vehicle model in the high fidelity vehicle dynamics simulation program Carsim Real Time with Sensors and Traffic is used along with two DSRC modems emulating the vehicle and pedestrian communications in the development and initial experimental testing of this method. The vehicle either stops or, if possible, goes around the pedestrians in a socially acceptable manner. The elastic band method is used to locally modify the vehicle trajectory in real time when pedestrians are detected on the nearby path of the vehicle. The effectiveness of the proposed method is demonstrated using hardware-in-the-loop simulations. 

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3. Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

   https://doi.org/10.1016/j.compenvurbsys.2023.101950  

   Hosseini, Maryam; Sevtsuk, Andres; Miranda, Fabio; Cesar, Roberto M.; Silva, Claudio T. (April 2023, Computers, Environment and Urban Systems)

   While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. 

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4. StreetBit: A Bluetooth Beacon-based Personal Safety Application for Distracted Pedestrians \

   https://doi.org/10.1109/CCNC49032.2021.9369650  

   Hasan, Raiful; Hoque, Mohammad Aminul; Karim, Yasser; Griffin, Russell; Schwebel, David; Hasan, Ragib (January 2021, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC),)

   null (Ed.)

   The safety of distracted pedestrians presents a significant public health challenge in the United States and worldwide. An estimated 6,704 American pedestrians died and over 200,000 pedestrians were injured in traffic crashes in 2018, according to the Centers for Disease Control and Prevention (CDC). This number is increasing annually and many researchers posit that distraction by smartphones is a primary reason for the increasing number of pedestrian injuries and deaths. One strategy to prevent pedestrian injuries and death is to use intrusive interruptions that warn distracted pedestrians directly on their smartphones. To this end, we developed StreetBit, a Bluetooth beacon-based mobile application that alerts distracted pedestrians with a visual and/or audio interruption when they are distracted by their smartphones and are approaching a potentially-dangerous traffic intersection. In this paper, we present the background, architecture, and operations of the StreetBit Application. 

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5. A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data

   https://doi.org/10.1109/SMC52423.2021.9658639  

   Islam, Muhammad Mobaidul; Newaz, Abdullah Al; Karimoddini, Ali (October 2021, 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC))

   This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection. 

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