Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Intentional motion on-line learning and prediction
Machine Vision and Applications
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Behavior Prediction Based on Obstacle Motion Patterns in Dynamically Changing Environments
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
Crowd counting and segmentation in visual surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Intelligent Transportation Systems
Detection Based Low Frame Rate Human Tracking
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles
IEEE Transactions on Robotics
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Obstacle avoidance in a dynamic environment: a collision cone approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Semantic-Based Surveillance Video Retrieval
IEEE Transactions on Image Processing
Learning intentions for improved human motion prediction
Robotics and Autonomous Systems
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Human motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments.