A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment
Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable...
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| Veröffentlicht in: | International journal of environmental research and public health 2020-11, Vol.17 (22), p.8438, Article 8438 |
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| description | Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device's error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method. |
| doi_str_mv | 10.3390/ijerph17228438 |
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Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device's error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.</description><subject>Accidental Falls</subject><subject>Accuracy</subject><subject>Algorithms</subject><subject>Cameras</subject><subject>Cities</subject><subject>Cracks</subject><subject>Edge detection</subject><subject>Environmental effects</subject><subject>Environmental factors</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>Gaussian process</subject><subject>Hazard identification</subject><subject>Inspection</subject><subject>Lasers</subject><subject>Life Sciences & Biomedicine</subject><subject>Light</subject><subject>Machine learning</subject><subject>Measurement techniques</subject><subject>Portable equipment</subject><subject>Public, Environmental & Occupational Health</subject><subject>Regression analysis</subject><subject>Risk Assessment</subject><subject>Roads & highways</subject><subject>Science & Technology</subject><subject>Segmentation</subject><subject>Stereoscopy</subject><subject>Walkways</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>ARHDP</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkc1PGzEQxa2qqHy01x4rSxzRUtuz649LpRABqYTEAdoeLa_tbZwm68XeEMFfj1EggltP86T5zZvRG4S-UnIKoMj3sPBpmFPBmKxBfkAHlHNS1ZzQj2_0PjrMeUEIyJqrT2gfgBHGAQ7QbIJ_hxxiX52Z7B2eDEOKxs5xFxO-Cc5vzPIfNr3Df4rYmAd8m8KAZ-bRJJfxJGef88r342e015ll9l9e6hH6dXF-O51VV9eXP6eTq8qCgLGSCmzTUM5q0UqoW2W550QaVbTrgDniqRLKGUIN7RraNp2gBBpra9m03MER-rH1HdbtyjtbViez1EMKK5MedDRBv-_0Ya7_xnstuOINF8Xg-MUgxbu1z6NexHXqy82a1ZxRAVKwQp1uKZtizsl3uw2U6Ofk9fvky8C3t3ft8NeoC3CyBTa-jV22wffW7zBCSKNqBiCKIrTQ8v_paRjNWH44jet-hCc4G6Aw</recordid><startdate>20201114</startdate><enddate>20201114</enddate><creator>Cohen, Rachel</creator><creator>Fernie, Geoff</creator><creator>Roshan Fekr, Atena</creator><general>Mdpi</general><general>MDPI AG</general><general>MDPI</general><scope>17B</scope><scope>AOWDO</scope><scope>ARHDP</scope><scope>BLEPL</scope><scope>DTL</scope><scope>DVR</scope><scope>EGQ</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope></search><sort><creationdate>20201114</creationdate><title>A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment</title><author>Cohen, Rachel ; 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| subjects | Accidental Falls Accuracy Algorithms Cameras Cities Cracks Edge detection Environmental effects Environmental factors Environmental Sciences Environmental Sciences & Ecology Gaussian process Hazard identification Inspection Lasers Life Sciences & Biomedicine Light Machine learning Measurement techniques Portable equipment Public, Environmental & Occupational Health Regression analysis Risk Assessment Roads & highways Science & Technology Segmentation Stereoscopy Walkways |
| title | A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment |
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