SCALE-ADAPTIVE REAL-TIME CROWD DETECTION AND COUNTING FOR DRONE IMAGES

We propose a scale-adaptive crowd detection and counting approach for drone images. Based on local feature points and density estimation considering the image scale, we detect dense crowds over multiple distances and introduce an extremely fast counting strategy with high accuracy for our detected crowd regions. We compare our results with a recent CNN-based state-of-the-art approach and validate both methods for different scaling factors on a novel crowd dataset. The results show that our proposed method outperforms the pre-trained CNN-based approach and receives very precise counting results for different zoom factors, resolutions and crowd sizes. Its low computational complexity makes it highly suitable for real-time analysis or embedded systems.