Main Article Content
Real-time people counting from video records are main building blocks for many applications in smart cities. Real-time human detection and tracking is a vast, challenging and important field of research. It has wide range of applications in human recognition, human computer interaction (HCI), video surveillance etc.This method is able to count people in real time and is robust to changes of illumination and background.People counting has a wide range of applications in the context of pervasive systems. There exist several vision-based algorithms for people counting. People counting is a spatio-temporal function of human sensing, which gives the count of people in a particular area. Counting people is a useful task, which helps in understanding the flow of people in various places. The knowledge of density of people over an area would be helpful in handling emergency situations, efficient allocation of resources in the smart buildings etc. The constant movement of people, different age groups and body types makes people counting a challenging process. In addition, the presence of obstacles in indoor spaces etc., and varying lighting conditions make the process of accurately estimating the number of people in an area at given time very difficult. To overcome the above issues, we propose a novel real-time people counting approach by TensorFlow.