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@article{154650,
        author = {Adithi S and Dhrithirhuth Rajanna  and K Rishika Ravi and Mahanth Sai M and Dr Rekha N},
        title = {Crowd Management Framework for Departure Control in Bus Transport Service using Image Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {821-827},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154650},
        abstract = {Crowd detection is an important aspect of video surveillance. Video surveillance systems are one of the most modern methods for estimating the density of people in a given area for providing facilities and obtaining human statistics. Factors such as severe occlusions, scene perspective distortions in real time application make this task a bit more challenging. Image recognition and classification using Convolution Neural Networks (CNN) are the two popular approaches used in object recognition systems. CNN models are built to evaluate its performance on image recognition and detection datasets. This paper develops a prototype of an intelligent public bus management system based on collecting data from surveillance cameras, processing image frames to estimate crowd density, and sending messages to bus depot as needed. Besides image processing algorithms, model consists of camera, software and WIFI for wireless data transmission at the Bus Depot. This system prevents the overcrowding of passengers, provide security, report passenger density data and thereby organize an effective bus management.},
        keywords = {Crowd Estimation, Video Surveillance, Convolution Neural Network (CNN), Occlusions, Image Processing, Image Segmentation.},
        month = {},
        }