MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos
<|reference_start|>MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos: Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end, we propose a Multi-Timescale Feature Learning (MTFL) method to enhance the representation of anomaly features. Short, medium, and long temporal tubelets are employed to extract spatio-temporal video features using a Video Swin Transformer. Experimental results demonstrate that MTFL outperforms state-of-the-art methods on the UCF-Crime dataset, achieving an anomaly detection performance 89.78% AUC. Moreover, it performs complementary to SotA with 95.32% AUC on the ShanghaiTech and 84.57% AP on the XD-Violence dataset. Furthermore, we generate an extended dataset of the UCF-Crime for development and evaluation on a wider range of anomalies, namely Video Anomaly Detection Dataset (VADD), involving 2,591 videos in 18 classes with extensive coverage of realistic anomalies.<|reference_end|>
@article{zhang2024mtfl:,
    title={MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly
  Detection in Surveillance Videos},
    author={Yiling Zhang, Erkut Akdag, Egor Bondarev, Peter H. N. De With},
    journal={arXiv preprint arXiv:2410.05900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05900},
    primaryClass={cs.CV}
}