Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation
This addresses shadow detection for surveillance systems, but appears incremental as it builds on existing methods for motion tracking and shadow segmentation.
The paper tackled the problem of shadows being misidentified as moving objects in surveillance videos, which degrades system performance, by proposing a method for segmenting cast and self shadows using interval-based Eigen value representation, achieving results tested on the IEEE CHANGE DETECTION 2014 dataset.
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.