Papers › Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition
Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition
Waqas Sultani, Imran Saleemi
This paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training. In this scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. We first explore reasons for poor classifier performance when tested on novel datasets, and quantify the effect of scene backgrounds on action representations and recognition. Using only the background features and partitioning of gist feature space, we show that the background scenes in recent datasets are quite discriminative and can be used classify an action with reasonable accuracy. We then propose a new process to obtain a measure of confidence in each pixel of the video being a foreground region, using motion, appearance, and saliency together in a 3D MRF based framework. We also propose multiple ways to exploit the foreground confidence: to improve bag-of-words vocabulary, histogram representation of a video, and a novel histogram decomposition based representation and kernel. We used these foreground confidences to recognize actions trained on one data set and test on a different data set. We have performed extensive experiments on several datasets that improve cross dataset recognition accuracy as compared to baseline methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | HMDBsmall-to-UCF | W. Sultani et al. | Accuracy | 68.67 | #3 of 3 | Archive leaderboard | report |
| Domain Adaptation | Olympic-to-HMDBsmall | W. Sultani et al. | Accuracy | 47.91 | #3 of 3 | Archive leaderboard | report |
| Domain Adaptation | UCF-to-HMDBsmall | W. Sultani et al. | Accuracy | 68.70 | #3 of 3 | Archive leaderboard | report |
| Domain Adaptation | UCF-to-Olympic | W. Sultani et al. | Accuracy | 33.33 | #3 of 3 | 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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