Learning Patterns of Activity Using Real-Time Tracking
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 Smoothing Filter for CONDENSATION
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Multiple People Tracking Using an Appearance Model Based on Temporal Color
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Computer vision techniques for PDA accessibility of in-house video surveillance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
DOTS: support for effective video surveillance
Proceedings of the 15th international conference on Multimedia
Occlusion analysis: Learning and utilising depth maps in object tracking
Image and Vision Computing
Consistent labeling for multi-camera object tracking
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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People tracking deals with problems of shape changes, self-occlusions and track occlusions due to other interfering tracks and fixed objects that hide parts of the people shape. These problems are more critical in indoor surveillance and in particular in home automation settings, in which the need to merge information obtained form different cameras distributed around the house calls for the integration of reliable data obtained during time. Therefore, tracking algorithms should be carefully tuned to cope with occlusions and shape changes, working not only at pixel level but also at region level. In this work we provide a novel technique for object tracking, based on probabilistic masks and appearance models. Occlusions due to other tracks or due to background objects and false occlusions are discriminated. The classification of occluded regions of the track is exploited in a selective model update. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel to track assignment even with large occlusions. At the same time, the model update is very reactive, so as to cope with sudden body motion and silhouette's shape changes. Due to its robustness, it has been used in different experiments of people behavior control in indoor situations.