Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
An Attribute-based Method for Video Anomaly Detection
Authors: Tal Reiss, Yedid Hoshen
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our method on three publicly available VAD datasets, using their training and test splits. Only test videos included anomalous events. We report the statistics of the datasets in Tab. 1. Our method achieves the highest performance on the three most popular public benchmarks. It simply consists of three simple representations and does not require training. Ablation study. We report in Tab. 3 the anomaly detection performance on the Ped2, Avenue and Shanghai Tech datasets of all attribute combinations. |
| Researcher Affiliation | Academia | Tal Reiss EMAIL School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel. Yedid Hoshen EMAIL School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel |
| Pseudocode | No | The paper describes the method using textual explanations and figures (e.g., Fig. 2 for an overview) rather than structured pseudocode or algorithm blocks. No section or figure is explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Our code is available at https://github.com/talreiss/Accurate-Interpretable-VAD. |
| Open Datasets | Yes | We evaluated our method on three publicly available VAD datasets, using their training and test splits. ... UCSD Ped2. This dataset (Mahadevan et al., 2010)... CUHK Avenue. This dataset (Lu et al., 2013)... Shanghai Tech Campus. This dataset (Liu et al., 2018a)... |
| Dataset Splits | Yes | We evaluated our method on three publicly available VAD datasets, using their training and test splits. Only test videos included anomalous events. We report the statistics of the datasets in Tab. 1. (Table 1 shows 'Total Train set' and 'Test set' frame counts for each dataset). |
| Hardware Specification | Yes | We carried out all our experiments on a NVIDIA RTX 2080 GPU. |
| Software Dependencies | No | The paper mentions several tools and models like 'Res Net50 Mask-RCNN', 'Flow Net2', 'Alpha Pose', and 'Vi T B-16 CLIP', but it does not specify software library versions (e.g., Python, PyTorch, TensorFlow, CUDA versions) that would be needed for replication. |
| Experiment Setup | Yes | Specifically for Ped2, Avenue, and Shanghai Tech, we set confidence thresholds of 0.5, 0.8, and 0.8. ... We use Hvelocity Wvelocity = 224 224 to rescale flow maps. ... We use B = 1 orientations for Ped2 and B = 8 orientations for Avenue and Shanghai Tech. ... When testing, for anomaly scoring we use k NN for the pose and deep representations with k = 1 nearest neighbors. For velocity, we use GMM with n = 5 Gaussians. |