Video Individual Counting for Moving Drones

• URL: http://arxiv.org/abs/2503.10701v1
• Date: Wed, 12 Mar 2025 07:09:33 GMT
• Title: Video Individual Counting for Moving Drones
• Authors: Yaowu Fan, Jia Wan, Tao Han, Antoni B. Chan, Andy J. Ma,
• Abstract summary: Video Individual Counting (VIC) has received increasing attentions recently due to its importance in intelligent video surveillance.<n>Previous crowd counting datasets are captured with fixed or rarely moving cameras with relatively sparse individuals.<n>We propose a density map based VIC method based on a MovingDroneCrowd dataset.
• Score: 51.429771128144964
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video Individual Counting (VIC) has received increasing attentions recently due to its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous crowd counting datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restricting evaluation for a highly varying view and time in crowded scenes. While VIC methods have been proposed based on localization-then-association or localization-then-classification, they may not perform well due to difficulty in accurate localization of crowded and small targets under challenging scenarios. To address these issues, we collect a MovingDroneCrowd Dataset and propose a density map based VIC method. Different from existing datasets, our dataset consists of videos captured by fast-moving drones in crowded scenes under diverse illuminations, shooting heights and angles. Other than localizing individuals, we propose a Depth-wise Cross-Frame Attention (DCFA) module, which directly estimate inflow and outflow density maps through learning shared density maps between consecutive frames. The inflow density maps across frames are summed up to obtain the number of unique pedestrians in a video. Experiments on our datasets and publicly available ones show the superiority of our method over the state of the arts for VIC in highly dynamic and complex crowded scenes. Our dataset and codes will be released publicly.

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