แนวคิดหลัก
The author proposes a unified model selection technique to automatically infer the number of motion groups for spectral clustering based motion segmentation methods by combining different existing model selection techniques. This approach aims to enhance the practicality and accuracy of motion segmentation in dynamic environments.
บทคัดย่อ
The content discusses a unified model selection technique for spectral clustering-based motion segmentation, addressing the challenge of automatically inferring the number of motion groups in dynamic scenes. By combining various model selection criteria, the proposed method aims to improve the accuracy and efficiency of motion segmentation algorithms. The evaluation on the KT3DMoSeg dataset demonstrates competitive results compared to baseline methods, showcasing the effectiveness of the unified model selection approach.
The paper introduces motion segmentation as a crucial task in computer vision applications like robotics and action recognition. Spectral clustering based methods have shown promising results but often require prior knowledge of the number of motions present in a scene, limiting their practicality. To address this limitation, a unified model selection technique is proposed to automate the process of inferring the number of motion groups by integrating multiple existing criteria.
The methodology section outlines how spectral clustering is used for motion segmentation based on object-specific point trajectories and optical flow cues. The proposed model selection method combines four widely used criteria - silhouette score, eigengap heuristic, Davies-Bouldin index, and Calinski-Harabasz index - to determine the optimal number of clusters for spectral clustering. Experiments conducted on the KT3DMoSeg dataset demonstrate competitive results compared to traditional approaches.
Further analysis delves into detailed comparisons between different model selection methods across varying numbers of motions in video sequences. The study highlights strengths and weaknesses based on MSE, prediction accuracy, and overall error rates on different types of motion affinity matrices. The proposed unified model selection technique shows promising performance across different scenarios but faces challenges with sequences containing four motion groups.
สถิติ
Table 1: Avg. MSE (higher is better) Aff. F: 1.091; Aff. OC: 1.364; Fused Aff.: 1.091; Average: 1.182
Table 2: Avg Acc (higher is better) Aff.F: 54.55; Aff.Oc: 45.45; Fused Aff.: 63.64; Average: 54.54
Table 3: Avg Error (lower is better) Aff.F: 13.89; Aff.Oc: 20.59; Fused Aff.:12..03; Average:15..50
คำพูด
"Motion segmentation is crucial in various applications such as robotics and action recognition."
"Spectral clustering methods have shown impressive results on motion segmentation in dynamic environments."
"Our method achieves competitive results compared to baseline approaches."