Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Segmentation and Tracking of Multiple Moving Objects for Intelligent Video Analysis
BT Technology Journal
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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In this paper we present a foreground segmentation and tracking system for monocular static camera sequences and indoor scenarios that achieves correct foreground detection also in those complicated scenes where similarity between foreground and background colours appears. The work flow of the system is based on three main steps: An initial foreground detection performs a simple segmentation via Gaussian pixel color modeling and shadows removal. Next, a tracking step uses the foreground segmentation for identifying the objects, and tracks them using a modified mean shift algorithm. At the end, an enhanced foreground segmentation step is formulated into a Bayesian framework. For this aim, foreground and shadow candidates are used to construct probabilistic foreground and shadow models. The Bayesian framework combines a pixel-wise color background model with spatial-color models for the foreground and shadows. The final classification is performed using the graph-cut algorithm. The tracking step allows a correct updating of the probabilistic models, achieving a foreground segmentation that reduces the false negative and false positive detections, and obtaining a robust segmentation and tracking of each object of the scene.