  Tracking of motion objects in the surveillance videos is useful for the
monitoring and analysis. The performance of the surveillance system will
deteriorate when shadows are detected as moving objects. Therefore, shadow
detection and elimination usually benefits the next stages. To overcome this
issue, a method for detection and elimination of shadows is proposed. This
paper presents a method for segmenting moving objects in video sequences based
on determining the Euclidian distance between two pixels considering
neighborhood values in temporal domain. Further, a method that segments cast
and self shadows in video sequences by computing the Eigen values for the
neighborhood of each pixel is proposed. The dual-map for cast and self shadow
pixels is represented based on the interval of Eigen values. The proposed
methods are tested on the benchmark IEEE CHANGE DETECTION 2014 dataset.
