BIAS: A Biologically Inspired Algorithm for Video Saliency Detection

• URL: http://arxiv.org/abs/2604.08858v1
• Date: Fri, 10 Apr 2026 01:45:47 GMT
• Title: BIAS: A Biologically Inspired Algorithm for Video Saliency Detection
• Abstract summary: We present BIAS, a fast, biologically inspired model for dynamic visual saliency detection in continuous streams.<n>Building on the Itti--Koch framework, BIAS incorporates a retina-inspired motion detector to extract temporal features.<n>FOAs are identified using a greedy multi-Gaussian motion peak-fitting algorithm that balances winner-take-all competition with information.
• Score: 0.6445605125467574
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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