SIAM Journal on Scientific and Statistical Computing
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
Background Modeling for Segmentation of Video-Rate Stereo Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new motion detection algorithm based on Σ-Δ background estimation
Pattern Recognition Letters
Wavelet-based data reduction for detection of moving objects
Machine Graphics & Vision International Journal
Spatio-temporal reasoning for the classification of satellite image time series
Pattern Recognition Letters
| Hi-index | 0.10 |
In video surveillance, detection of moving objects from an image sequence is very important for object tracking, activity recognition and behavior understanding. The conventional background subtraction suffers from slow updating of environmental changes, and temporal difference cannot accurately extract the moving object boundaries. In this paper, a Fourier reconstruction scheme for motion detection is proposed. A series of consecutive 2D spatial images along the time axis are first reorganized as a series of 2D spatial-temporal images along a spatial axis. In each of the 2D spatial-temporal images, a static background region forms a vertical line pattern, and a moving object creates an irregular, non-vertical structure in the image. Fourier transforms are applied to remove the vertical line pattern (i.e. the background) and retain only the foreground in the reconstructed image. The proposed method is a global approach that identifies the moving objects based on structural variations in the whole patterned image. It is therefore very robust to accommodate noise and local gray-level variations. It can well extract the shapes of foreground objects at various moving speeds, and is very responsive to dynamic environments. High computational cost is the major drawback of the proposed method. However, it can still achieve 11 frames per second for small images of size 150x200.