کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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411424 | 679558 | 2016 | 15 صفحه PDF | دانلود رایگان |
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.
Journal: Neurocomputing - Volume 197, 12 July 2016, Pages 14–28