Abstract—Cast shadows of moving foreground objects can
cause miss tracking problem in object detection and tracking,
thus shadow detection is an important step used after a moving
foreground object is detected. Most of current methods have a
significant trade-off between the shadow detection rate and the
shadow discrimination rate. In this paper, an effective and
adaptive method with combined texture and color models is
proposed in order to achieve good shadow detection rate and
shadow discrimination rate as well. Firstly, Scale Invariant
Local Ternary Pattern (SILTP) is used to select a candidate
shadow region. Then HSV color model is employed to detect a
new candidate shadow region by using maximum likelihood
estimation (MLE) to estimate the thresholds of HSV color
model adaptively. Finally the two regions are combined by
logical operation and a new shadow region can be obtained.
Our experimental results show that the proposed method
achieves a better performance in both shadow detection rate
and discrimination rate compared to the other current methods.
Moreover, the proposed method runs at 100 frames per second
and is suitable for the real-time detection and tracking.
Index Terms—Shadow detection, SILTP, HSV color space,
adaptive thresholds.
The authors are with the Communication & Information Security Lab,
Shenzhen Graduate School, Peking University, China (e-mail:
zhuys@pkusz.edu.cn).
[PDF]
Cite:Peiwen Liu and Yuesheng Zhu, "An Adaptive Cast Shadow Detection with Combined
Texture and Color Models," International Journal of Future Computer and Communication vol. 3, no. 2, pp. 113-118, 2014.
