Temporal Intention Localization (TIL) is crucial for video surveillance, focusing on identifying varying levels of suspicious intentions to improve security monitoring. However, existing discrete classification methods fail to capture the continuous nature of suspicious intentions, limiting early intervention and explainability. In this paper, we propose the Suspicion Progression Analysis Network (SPAN), which shifts from discrete classification to continuous regression, enabling the capture of fluctuating and evolving suspicious intentions. We reveal that suspicion exhibits long-term dependencies and cumulative effects, similar to Temporal Point Process (TPP) theory. Based on these insights, we define a suspicion score formula that models continuous changes while accounting for temporal characteristics. We also introduce Suspicion Coefficient Modulation, which adjusts suspicion coefficients using multimodal information to reflect the varying impacts of suspicious actions. Additionally, the Concept-Anchored Mapping method is proposed to link suspicious actions to predefined intention concepts, offering insights into both the actions and their potential underlying intentions. Extensive experiments on the HAI dataset show that SPAN significantly outperforms existing methods, reducing MSE by 19.8% and improving average mAP by 1.78%. Notably, SPAN achieves a 2.74% mAP gain in low-frequency cases, demonstrating its superior ability to capture subtle behavioral changes. Compared to discrete classification systems, our continuous suspicion modeling approach enables earlier detection and proactive intervention, greatly enhancing system explainability and practical utility in security applications.

翻译：时序意图定位在视频监控中至关重要，其核心在于识别不同层级的可疑意图以提升安防监控效能。然而，现有离散分类方法难以捕捉可疑意图的连续性特征，限制了早期干预能力与可解释性。本文提出嫌疑演化分析网络，将建模范式从离散分类转向连续回归，从而能够捕捉波动演化的可疑意图。我们发现嫌疑状态呈现长期依赖性与累积效应，其时序特性与时间点过程理论具有相似性。基于此，我们构建了融合时序特征的嫌疑评分公式以刻画连续变化过程。同时引入嫌疑系数调制机制，利用多模态信息动态调整嫌疑系数以反映不同可疑行为的差异化影响。此外，提出概念锚定映射方法，将可疑行为与预定义的意图概念相关联，从而同时揭示行为表象及其潜在意图。在HAI数据集上的大量实验表明，SPAN显著优于现有方法：均方误差降低19.8%，平均mAP提升1.78%。值得注意的是，SPAN在低频案例中实现了2.74%的mAP增益，证明其捕捉细微行为变化的卓越能力。相较于离散分类系统，我们的连续嫌疑建模方法能够实现更早的检测与主动干预，极大提升了安防应用中的系统可解释性与实用价值。