HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway
Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Gadjah Mada
2020-07-01
|
| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/54050 |
| _version_ | 1818446028190253056 |
|---|---|
| author | Firnanda Al Islama Achyunda Putra Fitri Utaminingrum Wayan Firdaus Mahmudy |
| author_facet | Firnanda Al Islama Achyunda Putra Fitri Utaminingrum Wayan Firdaus Mahmudy |
| author_sort | Firnanda Al Islama Achyunda Putra |
| collection | DOAJ |
| description | Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car. |
| first_indexed | 2024-12-14T19:41:12Z |
| format | Article |
| id | doaj.art-e9a9851d94454cce8cdb0e6e6a9d215e |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-14T19:41:12Z |
| publishDate | 2020-07-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-e9a9851d94454cce8cdb0e6e6a9d215e2022-12-21T22:49:42ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582020-07-0114323124210.22146/ijccs.5405027448HOG Feature Extraction and KNN Classification for Detecting Vehicle in The HighwayFirnanda Al Islama Achyunda Putra0Fitri Utaminingrum1Wayan Firdaus Mahmudy2Universitas BrawijayaFaculty of Computer Science, Universitas Brawijaya, MalangFaculty of Computer Science, Universitas Brawijaya, MalangAutonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.https://jurnal.ugm.ac.id/ijccs/article/view/54050histogram of oriented gradient (hog)k-nearest neighbour (knn)vehicle detection |
| spellingShingle | Firnanda Al Islama Achyunda Putra Fitri Utaminingrum Wayan Firdaus Mahmudy HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway IJCCS (Indonesian Journal of Computing and Cybernetics Systems) histogram of oriented gradient (hog) k-nearest neighbour (knn) vehicle detection |
| title | HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway |
| title_full | HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway |
| title_fullStr | HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway |
| title_full_unstemmed | HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway |
| title_short | HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway |
| title_sort | hog feature extraction and knn classification for detecting vehicle in the highway |
| topic | histogram of oriented gradient (hog) k-nearest neighbour (knn) vehicle detection |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/54050 |
| work_keys_str_mv | AT firnandaalislamaachyundaputra hogfeatureextractionandknnclassificationfordetectingvehicleinthehighway AT fitriutaminingrum hogfeatureextractionandknnclassificationfordetectingvehicleinthehighway AT wayanfirdausmahmudy hogfeatureextractionandknnclassificationfordetectingvehicleinthehighway |