Tracking Vehicles using Multiple Detections from a Monocular Camera
This thesis concerns image based tracking of vehicles using a monocular camera. A classifier is used to detect and classify objects in the images from the camera. For each detected object the classifier outputs several classifications, each including a confidence value. The objective of this thesis...
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Avdelningen för systemteknik
2015
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263824 |
| Summary: | This thesis concerns image based tracking of vehicles using a monocular camera. A classifier is used to detect and classify objects in the images from the camera. For each detected object the classifier outputs several classifications, each including a confidence value. The objective of this thesis is to investigate how these classifications and confidence values can be used in a single target tracking framework in the best possible way. This is achieved by evaluating several tracking methods that utilize the classifications and confidence values in different ways. The relationship between the confidence values and the accuracy of the corresponding classifications is also investigated. The methods are evaluated using data from real-world scenarios. It is found that classifications with high confidence values are more accurate on average than those with low confidence values. The differences in the average performance for the considered methods are found to be small. Image based tracking of vehicles is a key component in active safety systems in vehicles. Such systems can warn the driver or automatically brake the vehicle if a collision is about to happen, thereby preventing accidents. |
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