A Comparative Study of Vision Based Human Detection Techniques in People Counting Applications

People counting has a wide range of applications in the context of pervasive systems. These applications range from efficient allocation of resources in smart buildings to handling emergency situations. There exist several vision based algorithms for people counting. Each algorithm performs differently in terms of efficiency, flexibility and accuracy for different indoor scenarios. Hence, evaluating these algorithms with respect to different application scenarios, environment conditions and camera orientations will provide a better choice for actual deployment. For this purpose, in our paper the most commonly implemented Frame Differencing, Circular Hough Transform and Histogram of Oriented Gradient based methods are evaluated with respect to different factors like camera orientation, lighting, occlusion etc. The performance of these algorithms under different scenarios demonstrates the need for more accurate and faster people counting algorithms.