Abstract: This disclosure relates to method and system for monitoring activities and events in real-time. The method includes receiving video data of an area from each of one or more cameras. The video data includes a plurality of frames. For each frame of the plurality of frames, the method further includes generating in real-time, a space-time-behaviour dataset corresponding to the frame; identifying in real-time, at least one of an event from a plurality of predefined events, or an associated cause of an event from a plurality of predefined causes, based on the space-time-behaviour dataset; determining in real-time, a set of cause parameters or a set of event parameters, based on the space-time-behaviour dataset; and determining in real-time, whether at least one of the cause or the event corresponds to suspicious activity based on the comparison with parameters of the plurality of predefined causes and the plurality of predefined events.

Abstract: Disguised Face Identification (DFI) system and method for identifying multiple individuals with disguised faces in uncontrolled environments/scenarios is provided. The Disguised Face Identification (DFI) system and method includes detecting facial landmarks/facial key-points and performing face identification using the ScatterNet Hybrid Deep Learning (SHDL) Network. The system also can be evolved, after deployment, by the user as it provides one with an ability to add new faces to a known face database which are identified by the system thereafter. Further includes two facial disguise (FG) datasets, the datasets are simple facial disguise (FG) datasets and complex facial disguise (FG) datasets for training the deep convolutional networks.

Abstract: A real-time identification of suspicious individuals during night in public areas using a video brightening network system and method is provided in the present invention. The video brightening network system is a Generative Adversarial Network (GAN) that converts a very dark (night like) input video/input image (recorded from a standard RGB camera) into a bright (day like) output video/output image allowing a law enforcement to better monitor the scenes.

Abstract: Disguised Face Identification (DFI) system and method for identifying multiple individuals with disguised faces in uncontrolled environments/scenarios is provided. The Disguised Face Identification (DFI) system and method includes detecting facial landmarks/facial key-points and performing face identification using the ScatterNet Hybrid Deep Learning (SHDL) Network. The system also can be evolved, after deployment, by the user as it provides one with an ability to add new faces to a known face database which are identified by the system thereafter. Further includes two facial disguise (FG) datasets, the datasets are simple facial disguise (FG) datasets and complex facial disguise (FG) datasets for training the deep convolutional networks.

Abstract: A real-time aerial suspicious analysis (ASANA) system and method for identifying individuals engaged in suspicious activities in public areas is provided in the present invention. The aerial suspicious analysis (ASANA) uses a Drone for constant capturing and recording images/videos, and/or can be activated to capture/record based on a specific schedule and/or event; a YOLO detector to detect the individuals; a SHDL network for individual pose estimation, and then classification is performed of the estimated pose to identify the suspicious/violent individuals.