| Summary: | 碩士 === 國立清華大學 === 資訊工程學系所 === 105 === The challenges faced by many experiments with the vehicles detection at the urban intersections are that a bounding box is manually given to circle out the target object in the first frame and that the lost target object during a procedure of tracking might lead to tracking error. Hence, the specific objective of this thesis is to explore some solutions to these problems.
We apply the nonparametric scene parsing method to the vehicles detection at the urban intersections to automatically find out the car and motorcycle objects in the first frame without manually giving a bounding box. Moreover, the annotation results of scene parsing can improve the lost object.
Many researches about the nonparametric scene parsing have been studied currently. The nonparametric scene parsing is a method to annotate a query image by transferring labels from the training data set. Referring to the method of [5], our proposed method firstly segments the images into superpixels. By means of calculating features, we can extract similar image set as the retrieval set from the training data set. In addition, we learn weights for each image in the training data set to minimize classification error using a leave-one-out strategy. In order to boost the classification of rare classes, we compute the semantic context of segments in the training data set and add the nearest rare class examples into the retrieval set. Finally, we compute the energy function in Markov Random Field (MRF) to label the query image. Since the scene of urban intersections is our main testing data set, we use background subtraction to extract foregrounds so as to reduce classification error.
Our experimental results show that combination with the nonparametric scene parsing and background subtraction can effectively solve the problems of the vehicles detection at the urban intersections.
|
|---|
