Developed an iOS application for detecting pedestrian based on computer vision and machine learning with C++. Collected dataset, Extracted features based on Haar-like, HOG and LBP algorithm under OpenCV and trained classifiers. Implemented Non-maximum Suppression algorithm to improve accuracy。 Utilized pre-image-processing, Armadillo library and GPU to accelerate the speed of processing. This application is able to be used in iPhone, iPad and any iOS devices and detect pedestrians accurately in real video frames.
CARVIEW |
Select Language
HTTP/2 200
date: Sat, 26 Jul 2025 22:24:36 GMT
content-type: text/html; charset=utf-8
vary: X-PJAX, X-PJAX-Container, Turbo-Visit, Turbo-Frame, X-Requested-With,Accept-Encoding, Accept, X-Requested-With
etag: W/"d5b488a4eb4338b951bb693076f6b892"
cache-control: max-age=0, private, must-revalidate
strict-transport-security: max-age=31536000; includeSubdomains; preload
x-frame-options: deny
x-content-type-options: nosniff
x-xss-protection: 0
referrer-policy: no-referrer-when-downgrade
content-security-policy: default-src 'none'; base-uri 'self'; child-src github.githubassets.com github.com/assets-cdn/worker/ github.com/assets/ gist.github.com/assets-cdn/worker/; connect-src 'self' uploads.github.com www.githubstatus.com collector.github.com raw.githubusercontent.com api.github.com github-cloud.s3.amazonaws.com github-production-repository-file-5c1aeb.s3.amazonaws.com github-production-upload-manifest-file-7fdce7.s3.amazonaws.com github-production-user-asset-6210df.s3.amazonaws.com *.rel.tunnels.api.visualstudio.com wss://*.rel.tunnels.api.visualstudio.com objects-origin.githubusercontent.com copilot-proxy.githubusercontent.com proxy.individual.githubcopilot.com proxy.business.githubcopilot.com proxy.enterprise.githubcopilot.com *.actions.githubusercontent.com wss://*.actions.githubusercontent.com productionresultssa0.blob.core.windows.net/ productionresultssa1.blob.core.windows.net/ productionresultssa2.blob.core.windows.net/ productionresultssa3.blob.core.windows.net/ productionresultssa4.blob.core.windows.net/ productionresultssa5.blob.core.windows.net/ productionresultssa6.blob.core.windows.net/ productionresultssa7.blob.core.windows.net/ productionresultssa8.blob.core.windows.net/ productionresultssa9.blob.core.windows.net/ productionresultssa10.blob.core.windows.net/ productionresultssa11.blob.core.windows.net/ productionresultssa12.blob.core.windows.net/ productionresultssa13.blob.core.windows.net/ productionresultssa14.blob.core.windows.net/ productionresultssa15.blob.core.windows.net/ productionresultssa16.blob.core.windows.net/ productionresultssa17.blob.core.windows.net/ productionresultssa18.blob.core.windows.net/ productionresultssa19.blob.core.windows.net/ github-production-repository-image-32fea6.s3.amazonaws.com github-production-release-asset-2e65be.s3.amazonaws.com insights.github.com wss://alive.github.com api.githubcopilot.com api.individual.githubcopilot.com api.business.githubcopilot.com api.enterprise.githubcopilot.com; font-src github.githubassets.com; form-action 'self' github.com gist.github.com copilot-workspace.githubnext.com objects-origin.githubusercontent.com; frame-ancestors 'none'; frame-src viewscreen.githubusercontent.com notebooks.githubusercontent.com; img-src 'self' data: blob: github.githubassets.com media.githubusercontent.com camo.githubusercontent.com identicons.github.com avatars.githubusercontent.com private-avatars.githubusercontent.com github-cloud.s3.amazonaws.com objects.githubusercontent.com release-assets.githubusercontent.com secured-user-images.githubusercontent.com/ user-images.githubusercontent.com/ private-user-images.githubusercontent.com opengraph.githubassets.com copilotprodattachments.blob.core.windows.net/github-production-copilot-attachments/ github-production-user-asset-6210df.s3.amazonaws.com customer-stories-feed.github.com spotlights-feed.github.com objects-origin.githubusercontent.com *.githubusercontent.com; manifest-src 'self'; media-src github.com user-images.githubusercontent.com/ secured-user-images.githubusercontent.com/ private-user-images.githubusercontent.com github-production-user-asset-6210df.s3.amazonaws.com gist.github.com; script-src github.githubassets.com; style-src 'unsafe-inline' github.githubassets.com; upgrade-insecure-requests; worker-src github.githubassets.com github.com/assets-cdn/worker/ github.com/assets/ gist.github.com/assets-cdn/worker/
server: github.com
content-encoding: gzip
accept-ranges: bytes
set-cookie: _gh_sess=0kyfYbF%2BljZqimdRMcU4Ya5IJYVTMA2LsIaFA%2FkXoqmONsCm1UdC3bk69O5cmWtmjFkXaUWDpO93DDbIAGSZwS69uZWvPiWqGFxpAFsf4ZoCUtZnD%2F08hMzbxRA%2BtVu5OQoAML9dHOiuhEEhMll8Yt1xe7unym%2F92mV452SJFJOjWAwit1a6lEu4FJ4ZcbR2fcRB7XdfTaNkdluJSkqZR1eT1Qy%2B79p8dkzBy3gA4m11g0SYvSzcmUATvE1K6aTyFok%2FvWog8lyuA5xMDFmd5g%3D%3D--pYbg4RzBI%2BDKcvLv--%2BXaHD%2BfvUjfujSaqDpw0QA%3D%3D; Path=/; HttpOnly; Secure; SameSite=Lax
set-cookie: _octo=GH1.1.751153069.1753568676; Path=/; Domain=github.com; Expires=Sun, 26 Jul 2026 22:24:36 GMT; Secure; SameSite=Lax
set-cookie: logged_in=no; Path=/; Domain=github.com; Expires=Sun, 26 Jul 2026 22:24:36 GMT; HttpOnly; Secure; SameSite=Lax
x-github-request-id: 8DA8:18EAF6:92A1FE:BFAB09:688555A4
GitHub - XingchenYu/pedestrian_detection_iosapp: Developed an iOS application for detecting pedestrian based on computer vision and machine learning with C++. Collected dataset, Extracted features based on Haar-like, HOG and LBP algorithm under OpenCV and trained classifiers. Implemented Non-maximum Suppression algorithm to improve accuracy。 Utilized pre-image-processing, Armadillo library and GPU to accelerate the speed of processing. This application is able to be used in iPhone, iPad and any iOS devices and detect pedestrians accurately in real video frames.
Skip to content
Navigation Menu
{{ message }}
-
Notifications
You must be signed in to change notification settings - Fork 1
Developed an iOS application for detecting pedestrian based on computer vision and machine learning with C++. Collected dataset, Extracted features based on Haar-like, HOG and LBP algorithm under OpenCV and trained classifiers. Implemented Non-maximum Suppression algorithm to improve accuracy。 Utilized pre-image-processing, Armadillo library and…
XingchenYu/pedestrian_detection_iosapp
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
Developed an iOS application for detecting pedestrian based on computer vision and machine learning with C++. Collected dataset, Extracted features based on Haar-like, HOG and LBP algorithm under OpenCV and trained classifiers. Implemented Non-maximum Suppression algorithm to improve accuracy。 Utilized pre-image-processing, Armadillo library and…
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published
You can’t perform that action at this time.