• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5782-5799
• /
• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.10
• /
• pp.439-446
• /
• 2015

Recently, semantic trajectories which combine GPS positions and POIs(Point of Interests) become more popular in order to expand location based services. To construct semantic trajectories, the existing algorithms exploit the extent information of POIs described as polygons and find overlapping regions between GPS positions and the extents. However, the algorithms are not applicable in the condition where the extent information is not provided such as in Google Map, Naver Map, OpenStreetMap and most of the open geographic information systems. In this paper, we provide a novel algorithm to construct semantic trajectories only with GPS positions and POI points but without POI extents.

• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.346-353
• /
• 2011

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

• Journal of Broadcast Engineering
• /
• v.5 no.1
• /
• pp.41-49
• /
• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1726-1748
• /
• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Smart Media Journal
• /
• v.5 no.4
• /
• pp.26-40
• /
• 2016

Recent researches have been conducted for a user of the position acquisition and analysis since the mobile devices was developed. Trajectory data mining of location analysis method for a user is used to extract the meaningful information based on the user's trajectory. It should be preceded by a process of extracting Stay Point. In order to carry out trajectory data mining by analyzing the user of the GPS Trajectory. The conventional Stay Point extraction algorithm is low confidence because the user to arbitrarily set the threshold values. It does not distinguish between staying indoors and outdoors. Thus, the ambiguity of the position is increased. In this paper we proposed extraction method of Stay Point using a statistical analysis. We proposed algorithm improves position accuracy by extracting the points that are staying indoors and outdoors using Gaussian distribution. And we also improve reliability of the algorithm since that does not use arbitrarily set threshold.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.722-725
• /
• 2015

모바일 환경에서 사용자의 GPS 궤적은 위치기반서비스(Location Based Service)에서 새로운 자원으로써 활용되고 있다. 위치기반서비스의 확장을 위해 단순히 사용자의 위치를 지도에 표시하는 것뿐만 아니라 사용자들이 위치했던 장소들이 내포하고 있는 의미를 발견해 내는 것이 필요하다. 이를 위해 최근 사용자의 위치정보에 관심지점(POI: Point of Interest)의 정보를 결합하여 시맨틱 궤적(Semantic Trajectory)을 생성하고 분석하는 연구들이 진행되고 있다. 이러한 기존연구의 경우 시맨틱 궤적을 생성하기 위해, 사용자의 GPS 궤적과 POI의 면적 정보(polygon)가 겹칠 경우를 찾아내서 이를 시맨틱 궤적으로 생성하였다. 하지만 대부분 공개된 POI 정보는 실제 장소들의 면적 정보를 제공하지 않고 좌표(point) 값 만을 제공하기 때문에 기존의 방법으로는 시맨틱 궤적을 생성하지 못하는 문제가 있다. 본 논문에서는 사용자의 GPS 궤적과 POI의 좌표 값을 이용하여 사용자가 실제 방문했을 것으로 예상되는 POI 를 추정하고 이를 시맨틱 궤적으로 생성해 내는 방법을 제안한다. 제안하는 기법은 GPS 궤적의 속력 정보를 사용하여 사용자가 정지했었던 구간을 판별하고, 정지 구간 주변의 POI 밀도에 따라 정지 구간을 영역으로 확장한다. 그리고 영역에 포함된 POI 중 정지 구간과의 거리가 가장 가깝고, 가장 오랜 시간 포함되었던 POI를 사용자가 방문했던 POI로 판단한다. 이 방법은 POI의 면적정보가 없는 제한적인 상황에서도 시맨틱 궤적을 생성할 수 있다는 장점을 가진다.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.6
• /
• pp.95-104
• /
• 2015

Recently, collecting and analyzing method of users location has been studied due to the development of mobile devices. There is analyzing method using Semantic Location History in order to identify of characteristics and extract pattern and predict trajectory of users. We should extraction of Stay Point in order to use Semantic Location History. The Conventional extraction method of Stay Point is not accuracy of location of Stay Points because it does not specify the GPS log of users. Also, Conventional extraction method of Stay Point cannot distinguish indoors and outdoors. In this paper, we implement extraction method of Stay Point in which specify the GPS log of users and extraction of Stay Point at indoors only. Stay Point(nearSP) specifies the nearest GPS log of users from generated Stay Point by conventional extraction method. And, Stay Point(indoorSP) specifies the GPS log of users that user get into the building. Our experimental results, accuracy of Stay Point is improved, and capacity of output data decrease than Conventional extraction method. Also, we were able to distinguish Stay Point of indoors and outdoors.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2004.11a
• /
• pp.59-63
• /
• 2004

본 논문에서는 다양한 사용자 환경에서 비디오의 범용적인 서비스를 위한 다차원 비디오 트랜스코딩의 판단에 관하여 논한다 효율적인 판단을 위해 여러 영화 비디오 클립들을 비슷한 의미적 개념을 가지는 비디오들과 비슷한 장면 복잡도를 가지는 비디오들로 분류하고, 각 종류별로 주관적인 테스트(subjective test)를 실시하여 비디오 트랜스코딩에 있어서 사용자인지(perception)의 특성을 분석한다. 이렇게 분석된 인간의 시각 특성들을 이용해 비디오 트랜스코딩 판단 궤적(trajectory)을 만들고 이를 다차원 비디오 트랜스코딩 판단 시에 적용하기 위한 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.1254-1257
• /
• 2015

최근 위치기반서비스에 관심이 집중되면서 GPS 궤적에 관심 지점(POI: Point of Interest) 정보를 결합한 시맨틱 궤적(Semantic Trajectory)이 주목 받고 있다. 기존 연구에서는 GPS 궤적으로부터 속력을 계산하여 사용자가 정지했을 만한 예측 정지 구간(PSI: Predictive Stop Interval)과 실제로 방문했을 것이라 예상되는 POI를 선정하여 시맨틱 궤적을 생성하였다. 그러나 CPU에서는 대용량의 GPS 궤적에 대해서 PSI를 구할 시 많은 연산 때문에 시간이 오래 걸리는 문제가 있다. 이에 본 논문에서는 GPU의 병렬성을 이용하여 PSI를 생성하는 알고리즘을 제안한다. 제안하는 GPU를 이용한 PSI 생성 알고리즘은 기존의 CPU를 사용한 PSI 알고리즘보다 최대 5배 이상 속도 향상이 있으며, PSI의 개수가 많을수록 성능상의 이득이 더 큰 장점을 가지고 있다.