ED-DKCNN: Advancing Supervised Crowd Counting in Complex Environments

Abstract

The increasing urban population has led to challenges in managing crowd dynamics, especially preventing tragic incidents like stampedes. Real-time, accurate crowd counting faces obstacles such as background clutter and perspective variations. The study accepts these challenges of supervised crowd counting by examining the effectiveness of convolutional arrangements in improving accuracy by using an encoder-decoder dynamic convolutional neural network * (ED-DKCNN). The combined segmented, edge-oriented data and texture-rich features are the input for the model capable of precise crowd counting, achievable even in complex scenarios with occlusions and dense crowds. It explores low and high-level crowd features, addresses occlusion and uneven crowd distribution, and utilizes deep mining and dense complementarity for optimized people counting without density map estimation. The proposed framework harnesses intra-and inter-depth information representation through a non-increasing-order kernel arrangement, achieving state-of-the-art accuracy in people counting compared to existing methods across various datasets. The extensive experiments over free and surveillance category datasets through multiple evaluation criteria firmly * https://link.springer.com/article/10. establish the proposed ED-DKCNN model as a state-of-the-art performer in this domain. Moreover, the proposed model significantly advances crowd-counting methodologies, offering potential applications in multi-modal data integration, real-time scenarios, privacy concerns, edge computing, cross-domain situations, and human behavior.