Weakly-Supervised Spatiotemporal Anomaly Detection

Problem
This paper addresses the challenge of anomaly detection in video data using weak supervision, specifically focusing on the limitations of existing methods that require extensive frame-level annotations. The authors highlight the inefficiency of current approaches that rely on fully annotated datasets, which are often impractical to obtain. By proposing a weakly supervised framework that utilizes only video-level labels (normal or anomalous), the authors aim to fill the gap in the literature regarding effective anomaly detection without the need for detailed annotations. This work is presented as a preprint and has not yet undergone peer review.

Method
The proposed method employs a weakly supervised learning framework that leverages multiple instance learning (MIL) to classify spatiotemporal anomalies. The architecture consists of a feature extractor that processes video clips, which are categorized into positive bags (anomalous clips) and negative bags (normal clips). The MIL approach allows the model to learn from the weak labels by ranking the anomaly scores of different spatiotemporal regions within the clips. The authors implement a multiple instance ranking loss to optimize the model, which encourages the correct ranking of anomaly scores across the bags. The training process utilizes the UCF Crime2Local Dataset, which provides spatiotemporal annotations for a subset of the UCF Crime Dataset, allowing for the evaluation of the model’s performance on localized anomalies.

Results
The authors report significant improvements in anomaly detection performance compared to baseline methods. Specifically, they achieve an F1 score of 0.75 on the UCF Crime2Local Dataset, outperforming traditional supervised methods that require full annotations. The results indicate a 15% improvement over the best baseline, which underscores the effectiveness of the weakly supervised approach. Additionally, the model demonstrates robustness in detecting anomalies localized in specific regions of the video frames, further validating the proposed method’s capability in spatiotemporal contexts.

Limitations
The authors acknowledge several limitations in their work. Firstly, the reliance on weak video-level labels may lead to ambiguity in the training process, as the model cannot discern the specific locations of anomalies within the clips. Additionally, the performance may be affected by the quality and diversity of the training data, as the UCF Crime2Local Dataset may not encompass all possible anomaly types. The authors also note that the method may struggle with highly complex scenes where normal and anomalous behaviors are closely intertwined. An obvious limitation not discussed is the potential computational cost associated with processing high-resolution video data, which may hinder scalability in real-world applications.

Why it matters
This research has significant implications for the field of anomaly detection, particularly in scenarios where detailed annotations are impractical. By demonstrating that effective anomaly detection can be achieved with weak supervision, the work opens avenues for further exploration of unsupervised and semi-supervised learning techniques in video analysis. The findings could influence future research on scalable anomaly detection systems in various domains, including surveillance, healthcare, and autonomous systems, where timely identification of anomalies is critical.

Authors: Urvi Gianchandani, Praveen Tirupattur, Mubarak Shah
Source: arXiv:2605.13746
URL: https://arxiv.org/abs/2605.13746v1