Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thresholding for Change Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient hierarchical method for background subtraction
Pattern Recognition
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Neural Network Approach to Background Modeling for Video Object Segmentation
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
A new multiscale approach to motion based segmentation of objects in video sequences is presented. While image features extracted at multiple scales are commonly used within the pattern recognition community, they have seldom been employed for background modelling and subtraction. The paper describes a methodology for maintaining an explicit background model at multiple scales. Biological inspiration is used to contrive simple, yet effective mechanisms for feature extraction, incorporation of information across multiple scales and segmentation. Results of experiments conducted using sequences from the domain of traffic surveillance are presented in the paper. They suggest that the proposed method is able to achieve good segmentation results. In addition, the evaluated variant of a multiscale segmentation algorithm is far less computationally intensive, able to achieve processing of higher frame rates in real time and requires an order of magnitude less memory resources than the commonly-used approach compared against.