The Experts below are selected from a list of 33267 Experts worldwide ranked by ideXlab platform
Kosin Chamnongthai - One of the best experts on this subject based on the ideXlab platform.
-
Video shot boundary detection based on candidate segment selection and transition pattern analysis
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015Co-Authors: Sawitchaya Tippaya, Kosin Chamnongthai, Suchada Sitjongsataporn, Masood KhanAbstract:Video shot boundary detection or shot segmentation is an integral part of semantic video analysis. The objective of this process is to automatically detect the boundary region in video that further segment the video into meaningful shot, scene and so on. Video frame feature representation therefore plays an important role in the process where it directly affects the overall performance of the system. The transition points between meaningful scenes can be emphasised by the extracted features. In this paper, a combination of global and local feature descriptors is implemented to represent the Temporal Characteristic in video. Motivated by computational efficiency and practical implementation, a video shot boundary detection scheme using adaptive thresholding is proposed. Candidate segment selection and transition pattern analysis are implemented by the dissimilarity score between video frames. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.
-
A study of discriminant visual descriptors for sport video shot boundary detection
2015 10th Asian Control Conference (ASCC), 2015Co-Authors: Sawitchaya Tippaya, Masood Khan, Kosin ChamnongthaiAbstract:Video shot boundary detection is the process of automatically detecting the meaningful boundary content in video. Most shot boundary categorisation techniques use features extracted from the video frames to highlight the transition points between meaningful scenes. In this paper, a combination of global and local feature descriptors is proposed to represent the Temporal Characteristic in video. Motivated by the practical applications with moderate computational time, a video shot boundary detection scheme using supervised learning is proposed. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.
-
Abrupt shot boundary detection based on averaged two-dependence estimators learning
2014 14th International Symposium on Communications and Information Technologies (ISCIT), 2014Co-Authors: Sawitchaya Tippaya, Suchada Sitjongsataporn, Kosin ChamnongthaiAbstract:Video shot boundary detection is the process of automatically detecting the meaningful boundary in video data. It becomes an essential pre-processing step to video analysis, summarisation and other content-based retrieval. Video frame feature representation also plays an important role in the process where it directly affects to the performance of the system. Histogram dissimilarity-based with the pre-processed features scheme are proposed to represent the Temporal Characteristic in videos. Motivated by the practical applications with moderate computational time, supervised abrupt shot boundary detection with averaged two-dependence estimators probabilistic classification learning scheme is proposed in this paper. The performance evaluation is performed by TRECVID 2007 videos dataset containing various types of video category. The performance of the proposed scheme can be expressed in terms of precision and recall to detect the correct abrupt video shot.
Saad Rehman - One of the best experts on this subject based on the ideXlab platform.
-
Trajectory based vehicle counting and anomalous event visualization in smart cities
Cluster Computing, 2017Co-Authors: Fozia Mehboob, Muhammad Abbas, Richard Jiang, Abdul Rauf, Shoab A. Khan, Saad RehmanAbstract:Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-Temporal Characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.
Sawitchaya Tippaya - One of the best experts on this subject based on the ideXlab platform.
-
Video shot boundary detection based on candidate segment selection and transition pattern analysis
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015Co-Authors: Sawitchaya Tippaya, Kosin Chamnongthai, Suchada Sitjongsataporn, Masood KhanAbstract:Video shot boundary detection or shot segmentation is an integral part of semantic video analysis. The objective of this process is to automatically detect the boundary region in video that further segment the video into meaningful shot, scene and so on. Video frame feature representation therefore plays an important role in the process where it directly affects the overall performance of the system. The transition points between meaningful scenes can be emphasised by the extracted features. In this paper, a combination of global and local feature descriptors is implemented to represent the Temporal Characteristic in video. Motivated by computational efficiency and practical implementation, a video shot boundary detection scheme using adaptive thresholding is proposed. Candidate segment selection and transition pattern analysis are implemented by the dissimilarity score between video frames. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.
-
A study of discriminant visual descriptors for sport video shot boundary detection
2015 10th Asian Control Conference (ASCC), 2015Co-Authors: Sawitchaya Tippaya, Masood Khan, Kosin ChamnongthaiAbstract:Video shot boundary detection is the process of automatically detecting the meaningful boundary content in video. Most shot boundary categorisation techniques use features extracted from the video frames to highlight the transition points between meaningful scenes. In this paper, a combination of global and local feature descriptors is proposed to represent the Temporal Characteristic in video. Motivated by the practical applications with moderate computational time, a video shot boundary detection scheme using supervised learning is proposed. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.
-
Abrupt shot boundary detection based on averaged two-dependence estimators learning
2014 14th International Symposium on Communications and Information Technologies (ISCIT), 2014Co-Authors: Sawitchaya Tippaya, Suchada Sitjongsataporn, Kosin ChamnongthaiAbstract:Video shot boundary detection is the process of automatically detecting the meaningful boundary in video data. It becomes an essential pre-processing step to video analysis, summarisation and other content-based retrieval. Video frame feature representation also plays an important role in the process where it directly affects to the performance of the system. Histogram dissimilarity-based with the pre-processed features scheme are proposed to represent the Temporal Characteristic in videos. Motivated by the practical applications with moderate computational time, supervised abrupt shot boundary detection with averaged two-dependence estimators probabilistic classification learning scheme is proposed in this paper. The performance evaluation is performed by TRECVID 2007 videos dataset containing various types of video category. The performance of the proposed scheme can be expressed in terms of precision and recall to detect the correct abrupt video shot.
Jian Lu - One of the best experts on this subject based on the ideXlab platform.
-
traffic state spatial Temporal Characteristic analysis and short term forecasting based on manifold similarity
IEEE Access, 2018Co-Authors: Haobin Jiang, Xiaobo Chen, Jian LuAbstract:The study on the spatial-Temporal Characteristics of highway traffic flow is helpful to deeply understand the inherent evolution of highway traffic system and provide a theoretical basis for prediction and control of highway traffic flow. This paper makes an empirical analysis on the spatial-Temporal Characteristics of highway traffic flow using manifold similarity index and manifold learning technology. The time series of highway traffic flow is converted into the distance series containing manifold features to calculate the manifold distance between multi-section traffic flow data points, which are highly similar to spatial-Temporal distribution of traffic flow speed parameters, and then, the levels calibration of traffic state is carried out according to the manifold distance, so as to reveal the distribution rule of spatial-Temporal Characteristics of highway traffic flow. Its prediction error is obviously lower than the traditional distance measurement method, which has higher accuracy. The research of this paper can provide new ideas and methods to reveal the highway traffic flow evolution and traffic state prediction.
Song Ying - One of the best experts on this subject based on the ideXlab platform.
-
Study on Vehicle Navigation System with Real-Time Traffic Information
2008 International Conference on Computer Science and Software Engineering, 2008Co-Authors: Yu Yang, Song YingAbstract:The traditional vehicle navigation system is an isolated system, which can not meet the demanding of public traveling and traffic manage. Real-time traffic information is one of the most important applications for the driver and an essential feature of vehicle navigation system. Now today, most of the former navigation systems are developed based on static data instead of real-time or dynamic traffic information. In this paper, it gives the framework of vehicle navigation system based real-time traffic information, discusses spatial and Temporal Characteristic of real time navigation data and gets the real-time navigation data model in GIS-T, and successfully deploys it, which receives traffic information from the terrestrial digital multimedia broadcasting (T- DMB) system.
