Video surveillance has been one of the ubiquitous aspects of life since few decades. However, there are certain places that demand privacy of an individual like washrooms, changing rooms, baby feeding rooms at airports, etc where cameras cannot be installed / are restricted. Thus, it has raised concerns about safety and security of the public. The objective of our research is to design and analyze the processes and various conceptual models to automate the Entry-Exit surveillance of the people entering into or exiting from the Camera restricted areas. As part of the objective, in this paper, work is carried out to detect or determine the Entry-Exit events using the video frames captured at the entrances of the camera restricted areas by analyzing the variations in histograms of colors-RGB in the video frames using Histogram distance measures. Few grids in the Camera View Scene are selected by continuous learning and are extracted to determine the events happening in the scene thus contributing to improvement in computing time. Confirmation of event happening and classifying it as Entry or Exit or Miscellaneous is presented by temporal analysis of these grids. Experiments are conducted on few standard data sets like SBM datasets transforming them to our scenario, as well as our manual data sets captured in real time with few assumptions to test the techniques proposed.

Computer vision, video surveillance, camera prohibited areas ,color histograms, regression lines

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