Xinqiang Chen

TL;DR: This study comprehensively evaluated the multi-step prediction performance of models with different denoising algorithms using the traffic volume data collected from three loop detectors located on highway in city of Minneapolis to propose a prediction method by combiningDenoising schemes and support vector machine model to improve prediction accuracy.

TL;DR: An enhanced convolutional neural network (CNN) is proposed to improve ship detection under different weather conditions by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function.

TL;DR: A novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos by developing an ensemble detector to detect vehicles in the target region and a mapping algorithm to extract raw vehicle trajectories along the roadway curves.

TL;DR: The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network.

TL;DR: A framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively is proposed and shown to outperforms the conventional and typical ship tracking methods.