People counting has numerous applications across various domains, from urban planning to crowd management. With advances in computer vision, the use of cameras for real-time video analysis has increased significantly. While camera-based people counting is an effective and versatile approach, particularly in outdoor environments, it raises significant privacy concerns. MobileNet SSD is a widely used computer vision algorithm for detecting and classifying people in images. However, its real-time functionality relies on processing clear pictures of individuals, which can compromise privacy. This study investigates whether applying a blurring technique to video streams can enhance privacy while maintaining accurate people counting. To evaluate this, a video recording of individuals walking was processed with different blur levels and the accuracy of people counting was analyzed across all versions. Notably, one blurred video maintained an accuracy of 76%, equivalent to the original unblurred footage. Additionally, face detection was applied to the frames where people were identified and counted, revealing significantly lower detection accuracy for blurred images compared to human-level recognition. These results suggest that blurring can improve privacy while still enabling accurate people counting.