Papers › FOR THE SAKE OF PRIVACY: SKELETON-BASED SALIENT BEHAVIOR RECOGNITION
FOR THE SAKE OF PRIVACY: SKELETON-BASED SALIENT BEHAVIOR RECOGNITION
Thomas Golda, Johanna Thiemich, Mickael Cormier, Jurgen Beyerer
Authorities as well as emergency and rescue services have an increasing interest in smart support systems to ensure public safety which includes in particular behavioral analysis of pedestrians by using video surveillance systems. In order to accommodate concerns of citizens regarding their personal rights, the demand for data privacy friendly approaches, using as few information as possible, arises. In this paper, we examine existing approaches tackling the recognition of anomalous or salient behavior based solely on person pose information within the context of real-world surveillance applications. Particularly, we chose two existing state-of-the-art approaches and evaluate them on two public and an internal dataset in order to examine the overall performance of these methods for the desired task. Furthermore, we present our own approach achieving comparable results to these methods. Finally, we extend the aforementioned methods with a memory extension for modeling normal behavior, which yields on average a 4.3% higher recognition performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection In Surveillance Videos | VFP290K | MPED-RNN (with memory extension) | ROC AUC | 96.02 | #2 of 4 | Archive leaderboard | report |
| Anomaly Detection In Surveillance Videos | VFP290K | MPED-RNN | ROC AUC | 69.34 | #3 of 4 | Archive leaderboard | report |
| Anomaly Detection In Surveillance Videos | VFP290K | GEPC | ROC AUC | 64.03 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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