Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

Relationships between the hard and soft dimensions of the nose in Pan troglodytes and Homo sapiens reveal the nasal protrusions of Plio-Pleistocene hominids

By identifying similarity in bone and soft tissue covariation patterns in hominids, it is possible to produce facial approximation methods that are compatible with more than one species of primate. In this study, we conducted an interspecific comparison of the nasomaxillary region in chimpanzees and modern humans with the aim of producing a method for predicting the nasal protrusions of ancient Plio-Pleistocene hominids. We addressed this aim by first collecting and performing regression analyses of linear and angular measurements of nasal cavity length and inclination in modern humans ( Homo sapiens; n = 72) and chimpanzees ( Pan troglodytes ; n = 19), and then by performing a set of out-of-group tests. The first test was performed on two subjects that belonged to the same genus as the training sample, i.e., Homo ( n = 1) and Pan ( n = 1), and the second test, which functioned as an interspecies compatibility test, was performed on Pan paniscus ( n = 1), Gorilla gorilla ( n = 3), Pongo pygmaeus ( n = 1), Pongo abelli ( n = 1), Symphalangus syndactylus ( n = 3), and Papio hamadryas ( n = 3). We identified statistically significant correlations in both humans and chimpanzees with slopes that displayed homogeneity of covariation. Joint prediction formulae were found to be compatible with humans and chimpanzees as well as all other African great apes, i.e., bonobos and gorillas. The main conclusion that can be drawn from this study is that regression models for approximating nasal projection are homogenous among humans and African apes and can thus be reasonably extended to ancestors leading to these clades.

A Visual Tracking Algorithm Based on Estimation of Regression Probability Distribution

During target tracking, the target is often interfered by uncertainties like occlusion and motion blur. The interference leads to inaccurate tracking and even loss of the target. To solve the problem, this paper designs a target tracking algorithm based on the estimation of regression probability distribution (RPDE). Specifically, the uncertainty degree of the tracking frame was estimated by learning the statistical properties of regression parameters, and the quality of that frame was evaluated by fusing the predicted regression probability scores with classification scores. Next, an anchor-free regression mechanism was introduced to improve the computing speed. During network training, a simple and efficient strategy was presented for joint prediction, which jointly expresses classification scores and regression scores to eliminate the extra quality estimation branches in training and prediction. After that, the performance of our algorithm was tested on several public benchmarks, namely, OTB2015, VOT2016, GOT10k, and UAV123, and contrasted with several state-of-the-art algorithms. The results show that the proposed algorithm, named SiamRPDE for short, performed excellently on several benchmarks, and achieved the speed of 125 frames per second (FPS).

CNN-BASED MULTI-SCALE HIERARCHICAL LAND USE CLASSIFICATION FOR THE VERIFICATION OF GEOSPATIAL DATABASES

Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.