Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
Authors: Jie Wu, Wei Zhang, Guanbin Li, Wenhao Wu, Xiao Tan, Yingying Li, Errui Ding, Liang Lin
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task. |
| Researcher Affiliation | Collaboration | 1Sun Yat-sen University 2Baidu Inc. 3Byte Dance Inc. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links about the availability of source code. |
| Open Datasets | No | To this end, we build a new dataset (denoted as ST-UCF-Crime) that annotates spatio-temporal bounding boxes for abnormal events in UCF-Crime [Sultani et al., 2018]... Furthermore, we contribute a new dataset, namely Spatio-Temporal Road Accident (abbreviated as STRA)... The paper states they 'contribute' these datasets but does not provide concrete access information (e.g., a link, DOI, or repository) for the new annotations or the STRA dataset. It mentions 'We provide more details in the appendix', but the appendix is not provided. |
| Dataset Splits | No | The paper mentions 'We randomly choose 30 positive and 30 negative bags to construct a mini-batch' for training, and uses 'abnormal testing videos' for evaluation, but does not specify the explicit train/validation/test splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used, such as GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using 'C3D [Tran et al., 2015]' and 'Adam optimizer' but does not provide specific version numbers for these or other software libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We randomly choose 30 positive and 30 negative bags to construct a mini-batch, and the number of instances per bag is limited to 200. The total loss is optimized via Adam optimizer with the learning rate of 0.0005. |