Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation

TL;DR

This paper introduces a novel method for segmenting moving objects and distinguishing cast and self shadows in video sequences using interval-based eigenvalue representation, improving shadow detection accuracy in surveillance videos.

Contribution

It proposes a new eigenvalue-based approach for cast and self shadow segmentation in videos, enhancing shadow removal for better motion object tracking.

Findings

Effective shadow segmentation demonstrated on IEEE CHANGE DETECTION 2014 dataset

Improved accuracy in distinguishing shadows from moving objects

Method outperforms existing shadow detection techniques

Abstract

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.