Wide-View Sidewalk Dataset Based Pedestrian Safety Application

Sun, Chuen-Tsai; Su, Jia; Ren, Wenpeng; Guan, Yong

doi:10.1109/access.2019.2947165

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…This approach utilizes existing online map imagery as a primary data source. In contrast, the PESID [ 18 ] project concentrates on pedestrian safety, developing its dataset from a collection of labeled images depicting various sidewalk environments. Further diversifying the data collection techniques, ref.…”

Section: Related Workmentioning

confidence: 99%

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding

Akhavi Zadegan,

Vivet,

Hadachi

2024

Sensors

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Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors—the 4K camera, stereocamera, and LiDAR—each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments.

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Section: Related Workmentioning

confidence: 99%

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding

Akhavi Zadegan,

Vivet,

Hadachi

2024

Sensors

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“…An appropriative sidewalk dataset is established [8] to train a detection model that can be used to segment the sidewalk and driveway. The sidewalk dataset fully simulates the walking environment of phubbers, and it was collected on actual sidewalks from different districts and cities which includes Beijing, Tianjin, Shanghai.…”

Section: Sidewalk Datasetmentioning

confidence: 99%

Improvements for pedestrian safety application P-Minder

Sun

2022

EURASIP J. Adv. Signal Process.

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P-Minder is a lightweight sidewalk segmentation and obstacle detection-based approach designed for phubbers’ safety, which can help phubbers to avoid obstacles and other dangers during phubbing walking. But two issues have been found in previous experiments: P-Minder undifferentiated scores the recognition results and costs high resource occupation during background operation. Therefore, this paper proposes the sector correlation model to classify the recognition and segmentation results for an accurate judgment. Then, the new application architecture design and walking detection model decrease the resource occupation. Besides, the datasets are expanded, and the models are retrained to adapt to the improved architecture. In the same datasets, P-Minder achieves a 1.7 percentage point accuracy improvement during the experiment. The experimental result proves that the segmentation model can reach 81.2% mean intersection over union. Besides, the method finally achieves 75.52% detection accuracy in the experiment with a low computing resources cost.

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“…It is considered a driving assistance system that still requires user action. [13] 50K No annotations (classification) 5 Vistas [15] 25K Semantic polygon 37 PESID [14] 1.9K N/A * (seems bounding box) 10 CDBV [12] 13.5K N/A * (seems bounding box) 6 * We don't have access to the dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Obstacle Detection and Avoidance for Powered Wheelchair

Tawil

Hafez

2022

2022 Innovations in Intelligent Systems and Applications Conference (ASYU)

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Depth sensors like RGB-D cameras, LiDARs and laser scanners are widely investigated in research for Smart Wheelchair (SW) to carry out navigation, localization and obstacle detection and avoidance tasks. These sensors are costly compared to monocular camera sensor. A single off-the-shelf camera can be an economically efficient sensor to achieve obstacle detection and avoidance. We present in this paper a single camera based obstacle detection and avoidance method without using any 3D information. It is a novel vision-only system for wheelchair obstacle detection and avoidance that uses a Raspberry Pi along with Raspberry Pi camera. The obstacles are detected using a deep learning model built on MobileNetV2 SSD. The model is retrained using a dedicated dataset that was built for this purpose. Bounding boxes are used to mark detected obstacles; and feed them as features to the image space obstacle avoidance module. Figure 1 depicts internal view of what does the system see and an abstract description of our system's functionality.

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Wide-View Sidewalk Dataset Based Pedestrian Safety Application

Cited by 6 publications

References 23 publications

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding

Improvements for pedestrian safety application P-Minder

Deep Learning Obstacle Detection and Avoidance for Powered Wheelchair

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