Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles
Pedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2016-06-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.5772/62758 |
| _version_ | 1830288366973747200 |
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| author | Lie Guo Linhui Li Yibing Zhao Zongyan Zhao |
| author_facet | Lie Guo Linhui Li Yibing Zhao Zongyan Zhao |
| author_sort | Lie Guo |
| collection | DOAJ |
| description | Pedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize a Kalman filter to predict the possible position of the pedestrian centroid in the future frame. A Camshift tracking algorithm is used to track the pedestrian in the specific search window of the next frame based on the prediction results. The actual position of the pedestrian centroid is output from the Camshift tracking algorithm to update the gain and error covariance matrix of the Kalman filter. Experimental results in real traffic situations show the proposed pedestrian tracking algorithm can achieve good performance even when they are partly occluded in inconsistent illumination circumstances. |
| first_indexed | 2024-12-19T04:33:49Z |
| format | Article |
| id | doaj.art-a2e3af944ef64628b63400d1ff77ea6a |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-19T04:33:49Z |
| publishDate | 2016-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-a2e3af944ef64628b63400d1ff77ea6a2022-12-21T20:35:49ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-06-011310.5772/6275810.5772_62758Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous VehiclesLie Guo0Linhui Li1Yibing Zhao2Zongyan Zhao3 Dalian University of Technology, Dalian, China Dalian University of Technology, Dalian, China Dalian University of Technology, Dalian, China Dalian University of Technology, Dalian, ChinaPedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize a Kalman filter to predict the possible position of the pedestrian centroid in the future frame. A Camshift tracking algorithm is used to track the pedestrian in the specific search window of the next frame based on the prediction results. The actual position of the pedestrian centroid is output from the Camshift tracking algorithm to update the gain and error covariance matrix of the Kalman filter. Experimental results in real traffic situations show the proposed pedestrian tracking algorithm can achieve good performance even when they are partly occluded in inconsistent illumination circumstances.https://doi.org/10.5772/62758 |
| spellingShingle | Lie Guo Linhui Li Yibing Zhao Zongyan Zhao Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles International Journal of Advanced Robotic Systems |
| title | Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles |
| title_full | Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles |
| title_fullStr | Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles |
| title_full_unstemmed | Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles |
| title_short | Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles |
| title_sort | pedestrian tracking based on camshift with kalman prediction for autonomous vehicles |
| url | https://doi.org/10.5772/62758 |
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