An unsupervised approach for semantic place annotation of trajectories based on the prior probability

Cheng, Junyi; Zhang, Xianfeng; Luo, Peng; Huang, Jie; Jian-feng, Huang

doi:10.1016/j.ins.2022.06.034

Cited by 5 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…full-time worker). Recently, published work by Cheng et al (2022) presents an approach using semantic location annotation based on Point of Interest (POI) and Area of Interest (AOI). Furthermore, they include the temporal domain in their approach represented by features such as length of stay and visiting time.…”

Section: Related Workmentioning

confidence: 99%

“…The geometric data are provided as large trajectory data sets in Korea. While spatio-temporal analysis of movement patterns is of great interest, especially in the field of traffic and commuting smaller spatial and temporal frames can also be considered for example, in football analysis (Feuerhake, 2016). Moreover, spatio-temporal pattern mining is applied to non-trajectory data such as origin-destination (OD) data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

Golze,

Sester,

Feuerhake

et al. 2024

AGILE GIScience Ser.

View full text Add to dashboard Cite

Abstract. Over the past decade(s), collecting spatiotemporal data has become easier due to technological advancements and more user-friendly collection processes. Additionally, government agencies, companies, and open data projects have made general environmental data, such as points of interest or land use coverage, more freely available. Scientific studies can combine this spatiotemporal and non-spatial data to analyze different types of human mobility data. The results of these studies are relevant to transportation and urban planning, as similar information is typically collected by means of surveys. However, deriving relevant information from Global Navigation Satellite System (GNSS) trajectories remains challenging due to inaccuracies in the positioning and the unavailability of groundtruth information regarding individual user location semantics (e.g. home place, work place, leisure place or others). This work presents a semantic location annotation approach based on a Hidden Markov Model and the Viterbi optimization algorithm. The model includes location emissions to account for the general usage of a particular location. The annotations are applied to the clustered stop points that identify regions of special interest to individual users in a trajectory data set. The proposed approach demonstrates that the adapted Viterbi optimization can assign the most probable and meaningful semantic labels to the user’s sequences and provides insights on the underlying regions of special interest.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

Golze,

Sester,

Feuerhake

et al. 2024

AGILE GIScience Ser.

View full text Add to dashboard Cite

show abstract

“…Thus, the semantic annotation for visited places is needed after detecting stops. An unsupervised place annotation method [45] is used to infer the visited locations. In this method, a spatiotemporal probability model for the candidate places is created and decomposed into the spatial, duration, and visiting time probabilities.…”

Section: Algorithm For Detecting Unusual Visit Locationsmentioning

confidence: 99%

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Cheng

Zhang

Chen

et al. 2022

IJGI

Self Cite

View full text Add to dashboard Cite

Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.

show abstract

Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain

Giakoumoglou

Pechlivani

Tzovaras

2023

Smart Agricultural Technology

View full text Add to dashboard Cite

An unsupervised approach for semantic place annotation of trajectories based on the prior probability

Cited by 5 publications

References 41 publications

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain

Contact Info

Product

Resources

About