Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Training products of experts by minimizing contrastive divergence
Neural Computation
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Robust Similarity Measures for Mobile Object Trajectories
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
A Continuous Restricted Boltzmann Machine with a Hardware-Amenable Learning Algorithm
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Bi-Directional Tracking Using Trajectory Segment Analysis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Framework for real-time behavior interpretation from traffic video
IEEE Transactions on Intelligent Transportation Systems
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
A hierarchical self-organizing approach for learning the patterns of motion trajectories
IEEE Transactions on Neural Networks
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Motion trajectory is one of the most important cues for tracking and behavior recognition and can be widely applied to numerous fields. However, it is a difficult problem to directly model the spatio-temporal variations of trajectories due to their high dimensionality and nonlinearity. In this paper, we propose a joint trajectory tracking and recognition algorithm by combining a generative model derived from a bi-directional deep neural network (called ''autoencoder'') into a Bayesian estimation framework. The ''autoencoder'' network embeds high-dimensional trajectories into a two-dimensional plane based on a peculiar training rule and learns a trajectory generative model by its inverse mapping. A set of plausible trajectories can be generated by the trajectory generative model. In the tracking process, the samples from the plausible trajectory set are weighted by a mixed likelihood and are resampled to obtain the target state estimation at each time step in spirit of the particle filtering. The trajectory identity is inferred by evaluating the improved Hausdorff distance between the estimated trajectory up to now and the truncated reference trajectories. Moreover, the trajectory recognition results are also used to guide the trajectory tracking for the next time. The experiments on tracking and recognizing handwritten digits show that the proposed approach can achieve both robust tracking and exact recognition in background clutter and partial occlusion.