Learning representations by back-propagating errors
Neurocomputing: foundations of research
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Local Features for Image Retrieval
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Real-time object tracking with relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A survey on vision-based human action recognition
Image and Vision Computing
Enhanced human behavior recognition using HMM and evaluative rectification
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Action Recognition Using Spatial-Temporal Context
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new framework for on-line object tracking based on SURF
Pattern Recognition Letters
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on Pixel Change History (PCH) but focuses on the human body movements over time. We propose a modification of the conventional PCH which entails the calculation of two probabilistic maps, based on human face and body detection respectively. The features extracted from this descriptor are used as input to an HMM-based behavior recognition framework. We also introduce a rectification framework of behavior recognition and classification by incorporating an expert user's feedback into the learning process through two proposed schemes: a plain non-linear one and an adaptable one, which requires fewer training samples and is more effective in decreasing misclassification error. The methods presented are validated on a real-world computer vision dataset comprising challenging video sequences from an industrial environment.