Abstract
The ability to count people from video is a challenging problem. The scientific challenge arises from the fact that although the task is relatively well-defined, the imaging scenario is not well constrained. The background scene can be uncontrolled along with the illumination being complex and varying. Additionally, the spatial and temporal image resolution is usually poor. The context of most works in people counting is in counting pedestrians from single frames in outdoor settings or moving subjects in indoor settings from standard frame rate video. There is little work done on counting of persons in varying poses, who are mostly static (sitting, lying down), in very low frame rate video (4 frames per minute), and under harsh illumination variations. In this chapter, we explore a design that handles illumination issues at the pixel level using photometry-based normalization, and pose and low-movement issues at feature level by exploiting the spatio-temporal coherence that is present among small body part movements. The motion of each body part, such as the hands or the head, will be present even in mostly static poses. These short duration motions will occur spatially close together over the image location occupied by the subject. We accumulate these using a spatio-temporal autoregressive (AR) model to arrive at blob representations that are further grouped into people counts. We show quantitative performance on real datasets.
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Khemlani, A., Duncan, K., Sarkar, S. (2012). People Counter: Counting of Mostly Static People in Indoor Conditions. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_5
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DOI: https://doi.org/10.1007/978-3-642-28598-1_5
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