Gathering Identification Using Image Metrics for Intelligent Situation Awareness System in Real Time Scenarios

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition, etc. Face detection technology is applied for biometric authentication systems for face recognition and verification. This work aims at developing a tool that can be used in an Intelligent Situation Awareness System for a given Region of Interest. This has been done by identifying human face as objects from digital video frames through real time video streaming by using Recurrent Convolution Neural Network (RCNN) classifiers. The key frames from a video are identified and machine learning algorithm is being applied on it for performing the object identification. After the facial objects are being identified in a given frame, this can be utilized for performing semantic analysis in a given spatiotemporal scenario. The metrics identified in this work include object count, hand gestures, relative distance as well as density of objects for developing a robust system that could function in real time.

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