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A survey on next location prediction techniques, applications, and challenges

Ayele Gobezie Chekol, Marta Sintayehu Fufa
2022 EURASIP Journal on Wireless Communications and Networking  
Using this big location-based trajectory data, researchers tend to predict human next location.  ...  In next location prediction, trajectory is represented by a sequence of timestamped geographical locations.  ...  proposed Matrix Factorization on Semantic Trajectories for Predicting Future Semantic Locations [100] .  ... 
doi:10.1186/s13638-022-02114-6 fatcat:s2ixs3ftibaobighbik6ikgfce

INFER: INtermediate representations for FuturE pRediction [article]

Shashank Srikanth and Junaid Ahmed Ansari and Karnik Ram R and Sarthak Sharma and Krishna Murthy J. and Madhava Krishna K
2019 arXiv   pre-print
As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above).  ...  We propose intermediate representations that are particularly well-suited for future prediction.  ...  In [16] , the authors leverage scene semantics and the past motion trajectory to predict future trajectories.  ... 
arXiv:1903.10641v1 fatcat:6k65bv5puvfvlpjm7ht7sme6ye

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
The green line is the actual future trajectory and the yellow-orange heatmaps are example future predictions.  ...  Illustration of person trajectory prediction. (1) A person walks towards a car (data from the VIRAT/ActEV dataset).  ...  Our model utilizes multi-scale location decoders with graph attention model to predict multiple future locations.  ... 
doi:10.1109/cvpr42600.2020.01052 dblp:conf/cvpr/0001JMYH20 fatcat:u77gbdsgergxjc27emk5qlsqdm

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction [article]

Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann
2020 arXiv   pre-print
This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories.  ...  The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs.  ...  Our model utilizes multi-scale location decoders with graph attention model to predict multiple future locations.  ... 
arXiv:1912.06445v3 fatcat:luumpu6qmfeptndjdxqsexfkve

Multi-channel Convolutional Neural Networks for Handling Multi-dimensional Semantic Trajectories and Predicting Future Semantic Locations [chapter]

Antonios Karatzoglou
2020 Lecture Notes in Computer Science  
analyzing as well as in modeling human trajectories and predicting upon them.  ...  Current location-aware systems rely increasingly on location prediction techniques in order to provide their services in a timely fashion.  ...  In our future work, we plan to further explore the use of CNNs in the semantic location prediction scenario.  ... 
doi:10.1007/978-3-030-38081-6_9 fatcat:dmniy3dwjndezlopawilme24om

Peeking Into the Future: Predicting Future Person Activities and Locations in Videos

Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander Hauptmann, Li Fei-Fei
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Trajectory generator summarizes the encoded visual features and predicts the future trajectory by the LSTM decoder with focal attention [8] .  ...  Trajectory generator summarizes the encoded visual features and predicts the future trajectory by the LSTM decoder with focal attention [8] .  ... 
doi:10.1109/cvprw.2019.00358 dblp:conf/cvpr/Liang0NH019a fatcat:kf3fjokdxzey3fy7n3r2bg6i3y

Semantic trajectory mining for location prediction

Josh Jia-Ching Ying, Wang-Chien Lee, Tz-Chiao Weng, Vincent S. Tseng
2011 Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '11  
In this paper, we propose a novel approach for predicting the next location of a user's movement based on both the geographic and semantic features of users' trajectories.  ...  cluster determined by analyzing users' common behavior in semantic trajectories.  ...  OVERVIEW OF SemanPredict With the notion of semantic trajectory, we propose a novel location prediction framework, namely, SemanPredict, based on both the geographic and semantic features in trajectories  ... 
doi:10.1145/2093973.2093980 dblp:conf/gis/YingLWT11 fatcat:zezzbd46xrbb5hu2cycus3wh2m

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Antonios Karatzoglou, Dominik Köhler, Michael Beigl
2018 Sensors  
In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations.  ...  In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model.  ...  Conclusions and Future Work In this work, we evaluate the performance of three Markov Chain based models in modelling semantic trajectories and predicting future locations.  ... 
doi:10.3390/s18103582 fatcat:og52qbf4hngdjmdzovbjb7siru

SimAug: Learning Robust Representations from Simulation for Trajectory Prediction [article]

Junwei Liang, Lu Jiang, Alexander Hauptmann
2020 arXiv   pre-print
This paper studies the problem of predicting future trajectories of people in unseen cameras of novel scenarios and views.  ...  The key idea is to mix the feature of the hardest camera view with the adversarial feature of the original view. We refer to our method as SimAug.  ...  At test time, our model takes as input an agent's observable past (V 1:h , L 1:h ) in real videos to predict the agent's future locations L h+1:T = {y h+1 , . . . , y T }, scene semantic feature of view  ... 
arXiv:2004.02022v3 fatcat:xvjrrrmvsrgyxdojcjrrebs5ai

Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors

Manpreet Aneja Kaur, Flora Dilys Salim, Yongli Ren, Jeffrey Chan, Martin Tomko, Mark Sanderson
2020 ACM transactions on sensor networks  
We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction.  ...  To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's  ...  Context on Future Location Prediction.  ... 
doi:10.1145/3393692 fatcat:3nka56skpffcjcpjnczcizlgqi

A Spatial-Temporal-Semantic Method for Location Prediction in Indoor Spaces

Peng Wang, Jing Yang, Jianpei Zhang, Enrico M. Vitucci
2022 Wireless Communications and Mobile Computing  
Finally, by taking location context in the indoor environment (e.g., shop categories) into consideration, we successfully model and predict the user's future visiting points from the semantic perspective  ...  the constraint but filled with spatial-temporal-semantic info settings.  ...  To enhance the accuracy of our prediction model, we incorporate the SC (semantic section) prediction with trajectory prediction.  ... 
doi:10.1155/2022/5210005 fatcat:3axzd23mqrgwpnin2rejmbf76e

CAR-Net: Clairvoyant Attentive Recurrent Network [article]

Amir Sadeghian, Ferdinand Legros, Maxime Voisin, Ricky Vesel, Alexandre Alahi, Silvio Savarese
2018 arXiv   pre-print
., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories.  ...  We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment.  ...  [14] have demonstrated that the semantic segmentation of the environment (e.g., location of sidewalks and grass areas) helps to predict pedestrian trajectories. Ballan et al.  ... 
arXiv:1711.10061v3 fatcat:4a26ilrkpjamrmj3s4aj7xpega

CAR-Net: Clairvoyant Attentive Recurrent Network [chapter]

Amir Sadeghian, Ferdinand Legros, Maxime Voisin, Ricky Vesel, Alexandre Alahi, Silvio Savarese
2018 Lecture Notes in Computer Science  
., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories.  ...  We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment.  ...  [14] have demonstrated that the semantic segmentation of the environment (e.g., location of sidewalks and grass areas) helps to predict pedestrian trajectories. Ballan et al.  ... 
doi:10.1007/978-3-030-01252-6_10 fatcat:3mvgvqv2c5fr7f4dyxtzctbh5q

Social Behavior Prediction from First Person Videos [article]

Shan Su, Jung Pyo Hong, Jianbo Shi, Hyun Soo Park
2016 arXiv   pre-print
This paper presents a method to predict the future movements (location and gaze direction) of basketball players as a whole from their first person videos.  ...  We learn the egocentric visual semantics of group movements using a Siamese neural network to retrieve future trajectories.  ...  We leverage the visual social semantics embedded in first person cameras, which allows us to directly predict a plausible future group trajectory.  ... 
arXiv:1611.09464v1 fatcat:jhkdq3w535fwdftqs5qqhrg4f4

Recommendation System for High Utility Itemsets over Incremental Dataset
english

mozhi, D Manjula J K Kavitha, U Kani
2015 International Journal of Innovative Research in Science, Engineering and Technology  
The core idea of the cluster-based location prediction technique is to group users according to their similarity of semantic trajectory.  ...  The location prediction is based on the user trajectory whereas not on the basis of user preferences.  ...  Although semantic mining discovers users" semantic trajectory patterns, they cannot be used directly for location prediction since locations are not deductable from the semantic labels.  ... 
doi:10.15680/ijirset.2015.0403012 fatcat:ipw6qpjqyrbxxfl3mboklglzty
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