Tracking and data association
Performance of optical flow techniques
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Tracking with an Active Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using geometric corners to build a 2D mosaic from a set of image
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Real-Time Fixation, Mosaic Construction and Moving Object Detection from a Moving Camera
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spatially distributed model for foreground segmentation
Image and Vision Computing
Separation of Professional and Amateur Video in Large Video Collections
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Rejection of non-meaningful activities for HMM-based activity recognition system
Image and Vision Computing
Joint domain-range modeling of dynamic scenes with adaptive kernel bandwidth
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Journal of Signal Processing Systems
Spatiotemporal salience via centre-surround comparison of visual spacetime orientations
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Spatial codebook for robust background detection in visual information analysis
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
| Hi-index | 0.00 |
This paper proposes a new background subtraction method for detecting moving foreground objects from a nonstationary background. While background subtraction has traditionally worked well for a stationary background, the same cannot be implied for a nonstationary viewing sensor. To a limited extent, motion compensation for the nonstationary background can be applied. However, in practice, it is difficult to realize the motion compensation to sufficient pixel accuracy, and the traditional background subtraction algorithm will fail for a moving scene. The problem is further complicated when the moving target to be detected/tracked is small, since the pixel error in motion that is compensating the background will subsume the small target. A spatial distribution of Gaussians (SDG) model is proposed to deal with moving object detection having motion compensation that is only approximately extracted. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current flame is then classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the nonstationary background. Test cases involving the detection of small moving objects within a highly textured background and with a pantilt tracking system are demonstrated successfully.