Density-Assisted Adaptive Alignment Network for Video Individual Counting
TL;DR
The Density-assisted Adaptive Alignment Network (DAANet) is proposed, a novel framework that enables motion-adaptive and scale-aware alignment in VIC, and achieves state-of-the-art performance, outperforming prior methods and reducing Mean Squared Error by up to 31.6%.
Abstract
Video Individual Counting (VIC), which aims to enumerate unique individuals in a video sequence, is a vital problem with key applications in urban analytics, including transportation management and public safety. Existing methods often struggle with two interconnected challenges: scale ambiguity and motion diversity. The coexistence of diverse motion patterns and object scales challenges prior methods, which lack the flexibility to handle both simultaneously. To overcome these limitations, we propose the Density-assisted Adaptive Alignment Network (DAANet), a novel framework that enables motion-adaptive and scale-aware alignment. DAANet introduces two key modules: (1) a Density-driven Feature Enhancement (DFE) module that exploits density maps to construct spatial priors, and (2) a Hierarchical Deformable Feature Alignment (HDFA) module that allows multi-level features to adaptively align individuals based on their scale and motion characteristics. These components work in concert to overcome the fundamental challenges of VIC, leading to more accurate feature alignments and improved individual count estimates. Experimental results on three public VIC benchmarks show that DAANet achieves state-of-the-art performance, outperforming prior methods and reducing Mean Squared Error (MSE) by up to 31.6%. Source code is publicly available at https://github.com/LeoHuang0511/DAANet.