Abstract: Disclosed is a system (100) and method for tracking an inventory item (102, 202, 304) within a facility. The system comprises a unique identification tag (208, 306) for the inventory item and a plurality of cameras (104, 106, 204, 206) positioned within the facility. The unique identification tag is visible in images (302, 308, 312) captured by at least one of the plurality of cameras. An image processor (108) configured to receive at least one image of the inventory item from at least one of the plurality of cameras. The image processor identifies one or more parameters such as a type, a class, a state associated with the inventory item from the at least one image. The image processor further identifies the unique identification tag from the at least one image and identify the inventory item associated with it. The image processor employs at least one of: computer vision, neural networks, image processing algorithms for processing of the image.

Abstract: Disclosed is a system and a method for managing traffic in an environment comprising at least one operational vehicle and at least one autonomous vehicle therein.

Abstract: Disclosed is a system arranged to track a surgical kit comprising a container and one or more surgical tools. The system comprises a unique identification tag for container and a unique identification tag for one or more surgical tools; and an image processor configured to receive at least one image of unique identification tag for container and/or unique identification tag for one or more surgical tools captured by at least one imaging arrangement and process at least one image to: identify unique identification tag for container and/or one or more surgical tools, determine identity of container and/or one or more surgical tools based on one or more parameters identified from unique identification tag, and track a location of container and/or one or more surgical tools, based on a location of at least one imaging arrangement that captured the image of container and/or one or more surgical tools.

Abstract: Disclosed is a system for monitoring a subject. The system comprises image capturing arrangement comprising image sensor configured to capture one or more images of scan area; radar arrangement, operatively coupled to image capturing arrangement, wherein radar arrangement is configured to track subject in a region of scan area by using a cluster of radar reflections to detect macro-movement of the subject, define bounding box corresponding to the macro-movement, define aspect ratio of bounding box, and detect micro-movement of subject, when change in aspect ratio is observed; and processing arrangement configured to receive image data from image capturing arrangement and radar data from radar arrangement, analyse the image data and the radar data to determine the change in the aspect ratio, and trigger an emergency protocol when change in the aspect ratio is observed, or a value corresponding to the micro-movement differs from predefined range.

Abstract: Disclosed is a system and a method for monitoring pickup and drop-off of items in a storage facility, the system comprising a scanner disposed at an entry to the storage facility, the scanner configured to scan an item pickup code, and a controller, wherein the controller is configured to receive the scanned item pickup code, determine if there is an item associated with the item pickup code being stored in the storage facility; and, if so, grant entry to the storage facility to an item picker, receive one or more images of inside of the storage facility, determine an item picked by the item picker, determine that the item picked by the item picker corresponds to the item pickup code and, if not, generate a wrong pick-up signal.

Abstract: Disclosed is a method for augmenting a video footage of a surveillance space with a target three-dimensional (3D) object from one or more perspectives for training an artificial intelligence (AI) model, comprising: acquiring the video footage from a target camera in the surveillance space; determining a ground plane and screen coordinates of corners of the ground plane; normalizing screen coordinates from the ground plane and determining a relative position of each object in the ground plane; preparing a model of the target 3D object to be used for training the AI model; iteratively generating a random position and a random rotation for the target 3D object in the ground plane for positioning the target 3D object in front of or behind a distractor object from among the objects in the ground plane; rendering the model of the target 3D object on the ground plane and composing the rendered 3D object and the ground plane with the acquired video footage to generate a composited image; and calculating coordinates o

Abstract: Disclosed is a system (100) for processing images related to a target region. The system comprises a memory (102) and a computing arrangement (104) in communication with the memory. The computing arrangement is configured to define one or more target buffers (106) in the memory of a size corresponding to one or more image parameters to be used as an input to a model implemented for the processing the images. The computing arrangement is further configured to receive at least one image (300, 400A, 400B, 400C, 400D) of the target region from a wide-angle camera (108, 202), define two or more virtual camera views (302, 304, 306, 412, 416, 418, 420, 422) and process the at least one image to generate two or more virtual images (308, 310, 312, 400E) based on the defined one or more target buffers and the defined two or more virtual camera views. The computing arrangement is further configured to implement the said model to analyse the generated two or more virtual images.

Abstract: Disclosed is a system for monitoring an activity performed by the subject. The system comprises a non-imaging sensor configured to detect the subject in a scan area, wherein the subject is detected by reflected waveform thereby. The system also comprises a processing arrangement communicably coupled to the non-imaging sensor, wherein the processing arrangement is configured to receive the reflected waveform from the non-imaging sensor, employ a first neural network to estimate the skeletal pose of the subject, feed a temporal succession of a plurality of skeletal poses of the subject to a second neural network, and determine the activity performed by the subject based on the temporal succession of the plurality of skeletal poses. Disclosed also is a method for monitoring an activity performed by the subject.

Abstract: A method for tracking objects in an area is provided. The method comprises receiving a camera frame providing a view of at least one section of the area, selecting an object in the received camera frame and labelling the selected object to associate one or more tags with each of the one or more tags being assigned a value. The method further comprises maintaining a library of values assigned to the one or more tags and providing a user interface to allow an operator to select at least one value from the library of values assigned to the one or more tags to filter the corresponding one or more objects associated with the said at least one tag having the selected at least one value, for tracking the corresponding one or more objects in the area.