IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation

Samuel Cahyawijaya, Bryan Wilie, Widyawardana Adiprawita

Abstract


In crowd counting task, our goals are to estimate density map and count of people from the given crowd image. From our analysis, there are two major problems that need to be solved in the crowd counting task, which are scale invariant problem and inhomogeneous density problem. Many methods have been developed to tackle these problems by designing a dense aware model, scale adaptive model, etc. Our approach is derived from scale invariant problem and inhomogeneous density problem and we propose a dense aware inception based neural network in order to tackle both problems. We introduce our novel inception based crowd counting model called Inception Dense Estimator network (IDEnet). Our IDEnet is divided into 2 modules, which are Inception Dense Block (IDB) and Dense Evaluator Unit (DEU). Some variations of IDEnet are evaluated and analysed in order to find out the best model. We evaluate our best model on UCF50 and ShanghaiTech dataset. Our IDEnet outperforms the current state-of-the-art method in ShanghaiTech part B dataset. We conclude our work with 6 key conclusions based on our experiments and error analysis.

Keywords


Crowd Counting; Inception Network; Convolutional Neural Network; Deep Learning; Dense Aware; Scale Adaptive

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