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@article{151267,
        author = {Samarth Gupta and Vignesh S},
        title = {Study on Real-time Crowd Detection & Counting using deep learning in cloud},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {346-348},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151267},
        abstract = {Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring and so on .In the current computerized time, at numerous spots swarm checking instruments actually depend on older style techniques, for example, looking  after and so on and settling on educated choices on the premise regarding the quantity of individuals like food, water, identifying clog, and so forth A profound registers, utilizing individuals counters and sensors based checking at entrance. [1]These techniques come up short in where the convolution neural organization (DCNN) based framework can be utilized for close to ongoing group checking. The  framework utilizes NVIDIA development of individuals is totally irregular, exceptionally factor and dynamic. These strategies are tedious and drawn-out. The GPU processor to misuse  the equal figuring structure to accomplish quick and deft handling of the video feed taken through proposed framework is produced for circumstances where crisis departures are required, for example, fire flare-ups, cataclysmic occasions, a camera. This work contributes towards building a model to distinguish heads caught by CCTV camera. [2]
GPU processor to abuse the equal registering structure to accomplish quick and light-footed preparing of the video feed taken through a camera. This work contributes towards developing a model to identify heads caught by CCTV camera.
},
        keywords = {Crowd counting, compact convolutional neural network , cloud beanstalk.},
        month = {},
        }