Abstract:
Recently advanced vehicle re-identification frameworks are mainly based on convolutional neural networks (CNN) and labeled information. Previous frameworks face two diffi...Show MoreMetadata
Abstract:
Recently advanced vehicle re-identification frameworks are mainly based on convolutional neural networks (CNN) and labeled information. Previous frameworks face two difficulties. First CNN includes complicated architectures, which require expensive GPU devices to perform computation. The second difficulty is that annotating vehicle identities for every frame is expensive and time-consuming. To tackle these two difficulties, this study proposes a simple but effective method to perform re-ID without CNN and labeled identities. The proposed method has two streams of vehicle re-identification. The object detector takes charge of detecting vehicles on the road. With the position of vehicles in the image, the condition module extracts the vehicle movement information and sets the condition to match the same vehicle between current and subsequent frames. To train the object detector and test the proposed algorithm, a set of drone flight images collect and annotate for studying the traffic road. It contains 9,776 train images and 2,200 test images for object detection. In the experiments, three different traffic video clips were applied for testing the proposed method.
Date of Conference: 17-20 October 2022
Date Added to IEEE Xplore: 09 December 2022
ISBN Information: