生物启发模型BIAS实时检测视频显著区域，速度与精度均领先。
BIAS: A Biologically Inspired Algorithm for Video Saliency Detection
- 模仿视网膜设计运动检测器，融合静态与动态特征
- 毫秒级延迟下在DHF1K上超越深度学习模型
- 适合需要快速响应的交通事故预警等场景
我们提出BIAS，一种快速、生物启发的连续视频流动态视觉显著性检测模型。基于Itti-Koch框架，BIAS引入类视网膜运动探测器提取时序特征，生成融合静态与运动信息的显著图。通过贪心多高斯峰值拟合算法识别注意焦点（FOAs），在赢家通吃竞争与信息最大化间取得平衡。BIAS实现毫秒级延迟的显著区域检测，在DHF1K数据集上优于启发式方法和多个深度学习模型，尤其在以自下而上注意为主的视频中表现突出。应用于交通事故分析时，其在因果关系识别上达到当前最优性能，并能以可靠准确率提前0.72秒预测事故，早于人工标注。总体而言，BIAS实现了生物合理性与计算效率的统一，达成可解释、高速的动态显著性检测。
原文摘要 · Abstract (English)
We present BIAS, a fast, biologically inspired model for dynamic visual saliency detection in continuous video streams. Building on the Itti--Koch framework, BIAS incorporates a retina-inspired motion detector to extract temporal features, enabling the generation of saliency maps that integrate both static and motion information. Foci of attention (FOAs) are identified using a greedy multi-Gaussian peak-fitting algorithm that balances winner-take-all competition with information maximization. BIAS detects salient regions with millisecond-scale latency and outperforms heuristic-based approaches and several deep-learning models on the DHF1K dataset, particularly in videos dominated by bottom-up attention. Applied to traffic accident analysis, BIAS demonstrates strong real-world utility, achieving state-of-the-art performance in cause-effect recognition and anticipating accidents up to 0.72 seconds before manual annotation with reliable accuracy. Overall, BIAS bridges biological plausibility and computational efficiency to achieve interpretable, high-speed dynamic saliency detection.
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