Predicting the Unseen: A Novel Dataset for Hidden Intention Localization in Pre-abnormal Analysis

ZeHao Qi, Ruixu Zhang, Xinyi Hu, Wenxuan Liu, Zheng Wang

Abstract:

Our paper introduces a novel video dataset specifically designed for Temporal Intention Localization (TIL), aimed at identifying hidden abnormal intention in densely populated and dynamically complex environments. Traditional Temporal Action Localization (TAL) frameworks, focusing on overt actions within constrained temporal intervals, often miss the subtleties of pre-abnormal actions that unfold over extended periods. Our dataset comprises 228 videos with 5790 clips, each annotated to capture fine-grained actions within ambiguous temporal boundaries using a Joint-Linear-Assignment methodology. This comprehensive approach enables detailed analysis of the evolution of abnormal intention over time. To address the detection of subtle, hidden intention, we developed the Intention-Action Fusion module, an creative approach that integrates dynamic feature fusion across 11 behavioral subcategories, significantly enhancing the model's ability to discern nuanced intention. This enhancement has led to performance improvements of up to 139% in specific scenarios, dramatically boosting the model's sensitivity and interpretability, which is crucial for advancing the capabilities of proactive surveillance systems. By pushing the boundaries of current technology, our dataset and methodologies foster the development of proactive surveillance systems capable of preemptively identifying potential threats from nuanced behavioral patterns, encouraging further exploration into the complexities of intention beyond observable actions.

Primary Subject Area: [Engagement] Emotional and Social Signals

Relevance To Conference: Our research significantly advances the fields of multimedia and multimodal analysis by introducing a novel video dataset focused on the location and interpretation of subtle and complex abnormal intention. This dataset comprises 228 videos under surveillance with thousands of finely annotated clips designed to identify hidden abnormal intention within ambiguous temporal boundaries. We employ a Joint-Linear-Assignment methodology for a comprehensive analysis of abnormal intention and their evolution over time. This approach allows us to deeply analyze complex video data, enhancing our understanding of nuanced behaviors. Then we present the Intention-Action Fusion module, a breakthrough in recognizing potentially abnormal behaviors by examining fine-grained actions over extended periods. This module enhances our model's effectiveness in practical security applications by focusing on visual and temporal modalities.By providing these innovations, our work not only pushes the boundaries of traditional multimedia analysis, which often focuses on more static data, and offer guidance on security and deep insights for multimedia video analysis, but also contributes to the development of technologies capable of real-time, efficient surveillance in dynamically complex environments.

Submission Number: 2618