With the development of location-based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre-processed GPS data can provide the guarantee of high-quality data for the research of mining passenger's points of interest and urban computing services. The existing surveys mainly focus on map-matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open-source GPS sampling data sets, and the evaluation measurements of map-matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level features of human skeleton. However, low-level features are also important in many applications. For instance, lowlevel edge gradient and color information are important for image classificaion. This paper introduces a multi-branches structure to capture different low-level features of human skeleton. We combine both highlevel and low-level features to recognize human action. We validate our method in action recognition with two skeleton datasets, NTU-RGB+D and Kinetics. Experiment results indicate that the proposed method achieves considerable improvement over some state-of-the-art methods.
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