A background model re-initialization method based on sudden luminance change detection

2015 ◽  
Vol 38 ◽  
pp. 138-146 ◽  
Author(s):  
Fan-Chieh Cheng ◽  
Bo-Hao Chen ◽  
Shih-Chia Huang
Keyword(s):  
Change Detection ◽  
Background Model ◽  
Luminance Change

scholarly journals WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2672
Author(s):  
Wenhui Li ◽  
Jianqi Zhang ◽  
Ying Wang
Keyword(s):  
Change Detection ◽  
High Weight ◽  
State Of The Art ◽  
Diffusion Mechanism ◽  
Background Modeling ◽  
Background Model ◽  
Dynamic Background ◽  
Low Weight ◽  
Model Update ◽  
Sample Set

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.

New change detection tracking method based on background model

2007 ◽  
Author(s):  
Ruolan Hu ◽  
Xiao Zhou ◽  
Tao Zhang ◽  
Guilin Zhang
Keyword(s):  
Change Detection ◽  
Background Model ◽  
Tracking Method

Normal Spatial Attention But Impaired Saccades and Visual Motion Perception After Lesions of the Monkey Cerebellum

2009 ◽  
Vol 102 (6) ◽  
pp. 3156-3168 ◽  
Author(s):  
A. Ignashchenkova ◽  
S. Dash ◽  
P. W. Dicke ◽  
T. Haarmeier ◽  
M. Glickstein ◽  
...  
Keyword(s):  
Eye Movements ◽  
Motor Learning ◽  
Change Detection ◽  
Motion Perception ◽  
Cognitive Deficit ◽  
Visual Motion ◽  
Saccadic Eye Movements ◽  
Luminance Change ◽  
Visual Motion Perception ◽  
Saccadic Dysmetria

Lesions of the cerebellum produce deficits in movement and motor learning. Saccadic dysmetria, for example, is caused by lesions of the posterior cerebellar vermis. Monkeys and patients with such lesions are unable to modify the amplitude of saccades. Some have suggested that the effects on eye movements might reflect a more global cognitive deficit caused by the cerebellar lesion. We tested that idea by studying the effects of vermis lesions on attention as well as saccadic eye movements, visual motion perception, and luminance change detection. Lesions in posterior vermis of four monkeys caused the known deficits in saccadic control. Attention tested by examination of acuity threshold changes induced by prior cueing of the location of the targets remained normal after vermis lesions. Luminance change detection was also unaffected by the lesions. In one case, after a lesion restricted to lobulus VIII, the animal had impaired visual motion perception.

Depth cues affect orientation change detection, but not luminance change detection

2010 ◽  
Author(s):  
Stephen S. Killingsworth ◽  
Alex D. Franklin ◽  
Daniel T. Levin
Keyword(s):  
Change Detection ◽  
Luminance Change ◽  
Depth Cues ◽  
Orientation Change

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

Sign in / Sign up

Export Citation Format

Share Document