Artificial intelligence and machine learning Summit 2019 AI Conference India

Solutions that can generate accurate estimates of counts are in demand, whether it is for tallying the number of people in a video frame, counting the number of animals of an endangered species, estimating the number of objects or shapes in a picture, or for a variety of similar industry applications. Traditional crowd counting methods and models that use detection or regression-based approaches have been plagued by challenges such as occlusion, non-uniform distribution, perspective distortion, camera angles and background clutter. They are not robust and often fail with even simple changes to the planned scenarios. Deep learning based crowd counting solutions offer an excellent recourse to such problems. Cascaded CNN's use density-based estimations to preserve the spatial information and can localize the count, in addition to estimating the overall tally.