Objects Counting in Videos via Deep Learning and Image Processing

Objects counting in video frames is an important field of research that provides appropriate information about objects and their groups in video streams. An adequate technique to achieve this goal is using digital image/video processing methods with a detection method called CenterN et that is based on a deep learning architecture (i.e., a ConvNet architecture). This paper presents an objects counting method based on a combination of a detection model and optical flow generated between video frames.