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Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method. 

Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras. 

Building an efficient and accurate pixel-level labeling framework for large-scale and high-resolution satellite imagery is an important machine learning application in the remote sensing area. Due to the very limited amount of the ground-truth data, we employ a well-performing superpixel tessellation approach to segment the image into homogeneous regions and then use these irregular-shaped regions as the foundation for the dense labeling work. A deep model based on generative adversarial networks is trained to learn the discriminating features from the image data without requiring any additional labeled information. In the subsequent classification step, we adopt the discriminator of this unsupervised model as a feature extractor and train a fast and robust support vector machine to assign the pixel-level labels. In the experiments, we evaluate our framework in terms of the pixel-level classification accuracy on satellite imagery with different geographical types. The results show that our dense-labeling framework is very competitive compared to the state-of-the-art methods that heavily rely on prior knowledge or other large-scale annotated datasets.