Motion Segmentation via Global and Local Sparse Subspace Optimization

TL;DR

This paper introduces a novel motion segmentation framework that combines global sparse PCA and local subspace separation, effectively handling noise and missing data to improve segmentation accuracy and efficiency.

Contribution

The proposed method uniquely integrates global sparse PCA with local subspace search and error-based refinement for improved motion segmentation.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Reduces computation time significantly.

Effectively handles noisy and incomplete data.

Abstract

In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result and deal with the missing data problem, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset and Freiburg-Berkeley Motion Segmentation dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.