Abnormal crowd density estimation in aerial images based on the deep and handcrafted features fusion

Abstract

Abnormal crowd analysis has always been an interesting research area since it ensures people’s safety and prevents crowd disasters in large scale events. In particular, crowd density estimation in aerial images has the advantage of monitoring open areas by covering a very large view and reaching difficult access areas. In this context, we proposed a new method for crowd density estimation in aerial images in order to detect crowded areas showing abnormal densities. The proposed method consists of two phases: an offline phase and an inference phase. The offline phase aimed to generate a crowd model using a fusion of relevant deep and handcrafted features selected via the minimum-redundancy maximum-relevance (mRMR) technique. However, in the inference phase, we made use of the already generated model to classify the aerial images patches into four classes: None, Sparse, Medium and Dense. Our main contributions lie in the fact we relied on the fusion of semantic and low-level information to encode crowd density patches, as well as, the definition of the most salient crowd features to reduce confusion between crowd density classes. The experimental evaluation of the proposed method proves the validity of these contributions as well as the effectiveness and efficiency of our method compared to the state-of-the-art reference methods.