• AYUSH KUMAR TIWARI
• PROF. (DR.) NANDITA PRADHAN

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 de- tect 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 meth- ods 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 count- ing results for different zoom factors, resolutions and crowd sizes. Its low computational complexity makes it highly suit- able for real-time analysis or embedded systems.

crowd counting, crowd detection, drone, real-time, surveillance

"AI/ML REAL-TIME CROWD DETECTION AND COUNTING FOR UAV", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.a140-a144, June-2024, Available :http://www.jetir.org/papers/JETIR2406019.pdf

"AI/ML REAL-TIME CROWD DETECTION AND COUNTING FOR UAV", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppa140-a144, June-2024, Available at : http://www.jetir.org/papers/JETIR2406019.pdf