Scholarlink

<p>Multiple object tracking in dense scenes presents a significant challenge because of mutual occlusion and redundant detections, which can result in feature loss and cumulative error. To address these challenges, joint-detection-and-tracking frameworks based on the bipartite graph model have emerged as a popular paradigm. However, the bipartite graph model is often constrained by König’s minimax theorem, which postulates the need for one-to-one relationships in matching and equates the maximum number of matches with the minimum number of points covered. This restriction easily causes tracking failure for frequently appearing similarity targets. To overcome these limitations, this paper proposes a hypergraph random field model that uses domain hypernodes and trajectory hypernodes to differentiate targets on the basis of domain space features and trajectory fragments, respectively. This approach avoids cumulative error and enables one-to-many relationships for efficient tracking. Additionally, this paper presents an approximate solution that reduces the original complexity from <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(O(n^2)\)</EquationSource> </InlineEquation> to <i>O</i>(<i>n</i>). The experimental results on MOTChallenge demonstrate competitive performance, with a 1-2% improvement in MOTA compared with the baseline, particularly on MOT20, by almost 2%.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hypergraph random field model for multiple object tracking in dense scenarios

  • 期刊论文

  • 发表日期：

    2026年2月17日

查看全文
  • Junwen Zhang,
  • Xiaolong Zhang,
  • Ziqi Zhu,
  • Chunhua Deng

摘要

Multiple object tracking in dense scenes presents a significant challenge because of mutual occlusion and redundant detections, which can result in feature loss and cumulative error. To address these challenges, joint-detection-and-tracking frameworks based on the bipartite graph model have emerged as a popular paradigm. However, the bipartite graph model is often constrained by König’s minimax theorem, which postulates the need for one-to-one relationships in matching and equates the maximum number of matches with the minimum number of points covered. This restriction easily causes tracking failure for frequently appearing similarity targets. To overcome these limitations, this paper proposes a hypergraph random field model that uses domain hypernodes and trajectory hypernodes to differentiate targets on the basis of domain space features and trajectory fragments, respectively. This approach avoids cumulative error and enables one-to-many relationships for efficient tracking. Additionally, this paper presents an approximate solution that reduces the original complexity from \(O(n^2)\) to O(n). The experimental results on MOTChallenge demonstrate competitive performance, with a 1-2% improvement in MOTA compared with the baseline, particularly on MOT20, by almost 2%.