IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Introducing Termination Probabilities to HMM
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
An Appearance-Based Representation of Action
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Using Interaction Signatures to Find and Label Chairs and Floors
IEEE Pervasive Computing
Robust recognition and segmentation of human actions using HMMs with missing observations
EURASIP Journal on Applied Signal Processing
SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic player detection, labeling and tracking in broadcast soccer video
Pattern Recognition Letters
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper presents a method for finding and classifying objects within real-world scenes by using the activity of humans interacting with these objects to infer the object's identity. Objects are labelled using evidence accumulated over time and multiple instances of human interactions. This approach is inspired by the problems and opportunities that exist in recognition tasks for intelligent homes, namely cluttered, wide-angle views coupled with significant and repeated human activity within the scene. The advantages of such an approach include the ability to detect salient objects in a cluttered scene independent of the object's physical structure, adapt to changes in the scene and resolve conflicts in labels by weight of past evidence. This initial investigation seeks to label chairs and open floor spaces by recognising activities such as walking and sitting. Findings show that the approach can locate objects with a reasonably high degree of accuracy, with occlusions of the human actor being a significant aid in reducing over-labelling.