On the Discriminative Power of Learned vs. Hand-Crafted Features for Crowd Density Analysis


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. In particular, the approach consists of employing deep networks to extract useful crowd features that can further be manageable by a classifier. This process is favorable for crowd features extraction due to the large learning capability of deep networks compared to traditional methods based on handcrafted features. The proposed approach is evaluated on three challenging datasets, and the results demonstrate the effectiveness of learned features for crowd density classification. Furthermore, we include an extensive comparative study between different learned/hand-crafted features in order to investigate their discriminative power to handle such problems. Their �

In Theinternational joint conference on neural networks

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