BIAS: A Biologically Inspired Algorithm for Video Saliency Detection

TL;DR

BIAS is a fast, biologically inspired model for dynamic video saliency detection that combines static and motion cues, outperforming existing methods and demonstrating real-world utility in traffic accident prediction.

Contribution

The paper introduces BIAS, a novel biologically inspired algorithm that integrates motion detection and efficient attention modeling for real-time video saliency detection.

Findings

BIAS outperforms heuristic and deep-learning models on DHF1K dataset.

Achieves state-of-the-art results in traffic accident cause-effect recognition.

Detects salient regions with millisecond-scale latency.

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.