On the discriminative power of learned vs. hand-crafted features for crowd density analysis
Image credit: Unsplash Abstract Crowd density analysis is a crucial component in video surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density classification, in which learned features substitute the commonly used handcrafted features.
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