Temporally Consistent Dynamic Scene Graphs: An End-to-End Approach for Action Tracklet Generation
Raphael Ruschel, Md Awsafur Rahman, Hardik Prajapati, Suya You, B. S. Manjuanth

TL;DR
This paper introduces TCDSG, an end-to-end framework for generating temporally consistent dynamic scene graphs that track and link object interactions over time, significantly improving action tracklet generation in videos.
Contribution
It presents a novel bipartite matching mechanism with adaptive decoder queries for robust, temporally coherent scene graph generation across video sequences.
Findings
Achieves over 60% improvement in temporal recall@k on multiple datasets.
Establishes a new benchmark for dynamic scene graph generation.
Augments MEVA dataset with persistent object ID annotations.
Abstract
Understanding video content is pivotal for advancing real-world applications like activity recognition, autonomous systems, and human-computer interaction. While scene graphs are adept at capturing spatial relationships between objects in individual frames, extending these representations to capture dynamic interactions across video sequences remains a significant challenge. To address this, we present TCDSG, Temporally Consistent Dynamic Scene Graphs, an innovative end-to-end framework that detects, tracks, and links subject-object relationships across time, generating action tracklets, temporally consistent sequences of entities and their interactions. Our approach leverages a novel bipartite matching mechanism, enhanced by adaptive decoder queries and feedback loops, ensuring temporal coherence and robust tracking over extended sequences. This method not only establishes a new…
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Taxonomy
TopicsHuman Pose and Action Recognition · Human Motion and Animation · Anomaly Detection Techniques and Applications
