| dc.description.abstract | We combine the slow-moving self-propelled car with computer vision, hoping to enable the self-propelled car to track specific targets, and in the future, it can be used in sightseeing exhibitions or shopping malls, etc., instead of traditional manpower guides or ordinary shopping carts, increasing the convenience in life. To track a specific target for a long time, we need to cope with the target appearance and background change with time, and the wearer and appearance of the pedestrian is the classification basis of the general classifier, but the pedestrian is generally easy to change the wearing, so we jump out of the general classifier concept, not only reduce the time and the need of sample to train classifiers, but also identify the target.
The system is divided into two parts. The first part is the detection of pedestrians and obstacles, we will find out all the pedestrians and possible obstacles in the image. The second part is the confirmation of the leader, the pedestrians identified in the first part are compared with the target image in the database to determine the similarity of each other, used to identify the targets appearing in the image. The detection of pedestrians and the discrimination of pedestrians has not been effective in the past. Therefore, this study uses convolutional neural networks to practice these two tasks. The convolutional neural network captures image features and is more adaptive to the appearance of the object changes, it can maintain the accuracy of the detection with time. By training the network to calculate image similarity offline in advance, let the network learn to determine whether the two images are the same target, and do not need to train for specific targets. The network is possible to directly use on the untrained target online, and also adapt to the actual application needs of replacing the target in a short period of time.
In the experiment, we took the film to test leader tracking, using continuous frame to figure out if the system has found the right predecessor in each image, the accuracy of system is reached 95%, and our system will not be affected by other pedestrians or obstacles. If there are other pedestrians covering the predecessor, the proposed system can adapt well, with no misjudgment. | en_US |