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Dynamic attention priors: a new and efficient concept for improving object detection

Gepperth, Alexander R. T., Garcia Ortiz, M. ORCID: 0000-0003-4729-7457, Sattarov, E. & Heisele, B. (2016). Dynamic attention priors: a new and efficient concept for improving object detection. Neurocomputing, 197, pp. 14-28. doi: 10.1016/j.neucom.2016.01.036


Recent psychophysical evidence in humans suggests that visual attention is a highly dynamic and predictive process involving precise models of object trajectories. We present a proof-of-concept that such predictive spatial attention can benefit a technical system solving a challenging visual object detection task. To this end, we introduce a Bayes-like integration of the so-called dynamic attention priors (DAPs) and dense detection likelihoods, which get enhanced at predicted object positions obtained by the extrapolation of trajectories.

Using annotated video sequences of pedestrians in a parking lot setting, we quantitatively show that DAPs can improve detection performance significantly as compared to a baseline condition relying purely on pattern analysis.

Publication Type: Article
Additional Information: Visual attention, Object detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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