Abstract
In this paper, we implemented a vehicle verification system, which mainly use the concept of embedding to convert the vehicle's feature and then identify this vehicle. In addition to characteristics, the method for matching vehicle objects across cameras in this thesis is indirectly. The advantage of indirectly matching vehicle objects is to overcome the variations of size, aspect, and illumination obtained from cameras. Finally, we will experimentally test for different states to prove the stability of this system.First, we use background subtraction to extract the vehicle (which appear in the video) as the foreground region, and then we used the conditional morphological operations to fill up the broken region which is in the foreground. We do some operations for the vehicle location of this region, however, we must adjust the vehicle region which is be retrieved by us. This way can ensure that when the car drove to different region, the images are retrieved out of the vehicle can be normalized to the same center. After we obtained the vehicles foreground image, we can get the edge and color features, and then we contrast the distance between this features, and we can produce a set of distance feature matrix. Distance feature matrix is primarily intended to train example vehicles. After when we get example vehicle, we used these example vehicle as our basis for robust distance measure operation, and finally we get embedding feature matrix. The embedding feature matrix is used to train the SVM classifier which is used to identify. In the experiment, we take a video in the road scenarios in real life to measure the accuracy of vehicle identification system. According to the experimental results, we can determine that our systems can achieve very high accuracy.