Data collection, and especially data annotation, are surprisingly time consuming and costly tasks for vehicle classification. Annotation is used to label examples of vehicles, manually outlining their shapes and assigning their correct classification, for use in classifier training and performance evaluation. This study presents a semi‐automatic approach for the annotation of the vehicle samples recorded from roadside CCTV video cameras. Vehicles are detected by using automatic image analysis and classified into four main categories: car, van, bus and motorcycle/bicycle by using a vehicle observation vector constructed from the size, the shape and the appearance features. Unsupervised K ‐means clustering is used to automatically compute an initial class label for each detected vehicle. Then, in an iterative process, the output scores of a linear support vector machines classifier are used to identify the low confidence samples, for which the annotations are considered for manual correction. Experimental results are presented for both synthetic and real datasets to demonstrate the effectiveness and the efficiency of the authors approach, which significantly reduces the time required to generate an annotated dataset. The method is general enough that it can be used in other classification problems and domains that use a manually‐created ground‐truth.
Semi‐automatic annotation samples for vehicle type classification in urban environments
IET Intelligent Transport Systems ; 9 , 3 ; 240-249
2015-04-01
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
linear support vector machines classifier , roadside CCTV video cameras , unsupervised k‐means clustering , automatic image analysis , unsupervised learning , data annotation , data collection , urban environments , video cameras , vehicle type classification , closed circuit television , vehicle observation vector , traffic engineering computing , iterative process , annotated dataset , support vector machines , classifier training , image classification , pattern clustering , performance evaluation , road vehicles , semiautomatic annotation samples
Semi-automatic annotation samples for vehicle type classification in urban environments
IET | 2015
|Semi-automatic image annotation of street scenes
IEEE | 2017
|Semi-Automatic Image Annotation of Street Scenes
British Library Conference Proceedings | 2017
|