A system for physical-virtual environment fusion includes a sensor with a computing system and a memory in communication with the computing system, the memory storing a plurality of endpoints. The computing system is configured to select a semantic identity of an object from among a group of objects indicated by a user pointer indicator by determining, based on inputs from the sensor, the approximate orientation of the user pointer indicator towards a first endpoint from among the plurality of endpoints, the first endpoint having the group of objects located within the first endpoint. It further renders, on a display surface, the group of objects, receives from the user an indication regarding the semantic identity of the object and presents, on the display surface, an indication of the selection of the object from among the group of objects based on the further indications from the user.

Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

A moving object detection system detecting moving objects based on a captured image comprising: cameras capturing images to generate image data; image recognition circuitries each performing image recognition on the image data generated by each camera to detect a moving object in a captured image indicated by the image data; and an information management apparatus managing image data supplied from each camera to a corresponding image recognition circuitry. The information management apparatus includes a communication interface performing data communication with the cameras and the image recognition circuitries, and a controller managing congestion information on the number of moving objects localized within a range of a captured image indicated by image data for each camera, and controlling the communication interface to supply the image data generated by the cameras to each image recognition circuitry, distributing the image data according to the congestion information.

A crowd motion simulation method is provided based on real crowd motion videos. The method includes framing the videos and storing the framed videos into continuous high-definition images, generating a crowd density map of each image, and accurately positioning an individual in each density map to obtain the accurate position of each individual. The method also includes correlating the positions of each individual in different images to form a complete motion trajectory, and extracting motion trajectory data; and quantifying motion trajectory data, defining training data and data labels, and calculating data correlation. The method further includes building a deep convolutional neural network, and inputting the motion trajectory data for training to learn crowd motion behaviors; and randomly placing a plurality of simulation individuals in a two-dimensional space, testing a prediction effect of the deep convolutional neural network, adjusting parameters for simulation, and drawing a crowd motion trajectory.

Identifying activity in an area even during periods of poor visibility using a video camera and an audio sensor are disclosed. The video camera is used to identify visible events of interest and the audio sensor is used to capture audio occurring temporally with the identified visible events of interest. A sound profile is determined for each of the identified visible events of interest based on sounds captured by the audio sensor during the corresponding identified visible event of interest. Then, during a time of poor visibility, a subsequent sound event is identified in a subsequent audio stream captured by the audio sensor. One or more sound characteristics of the subsequent sound event are compared with the sound profiles associated with each of the identified visible events of interest, and if there is a match, one or more matching sound profiles are filtered out from the subsequent audio stream.

The embodiment of the present disclosure provides a method for setting time of traffic lights in a smart city and an Internet of Things system. The method is implemented by an Internet of Things system for setting time of traffic lights in a smart city, which includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The method includes: obtaining pedestrian information and intersection information of a target intersection, and the target intersection being an intersection provided with the traffic lights; determining, based on the pedestrian information and the intersection information, the scheme for setting time of the traffic lights at the target intersection; and sending a control instruction corresponding to the scheme for setting the time to the object platform, and in response to the received control instruction, controlling lighting duration of the traffic lights by the object platform.

A method for tracking one or more objects in a specific space is provided. The method includes steps of: (a) inputting original images of the specific space taken from camera to an obfuscation network and instructing the obfuscation network to obfuscate the original images to generate obfuscated images such that the obfuscated images are not identifiable as the original images by a human but the obfuscated images are identifiable as the original images by a learning network; (b) inputting the obfuscated images into the learning network, and instructing the learning network to detect obfuscated target objects, corresponding to target objects to be tracked, in the obfuscated images, to thereby output information on the obfuscated target objects; and (c) tracking the obfuscated target objects in the specific space by referring to the information on the obfuscated target objects.

Disclosed are a pedestrian re-identification method and apparatus based on local feature attention. The method includes the following steps: S1: obtaining an original surveillance video image data set, and dividing the original surveillance video image data set into a training set and a test set in proportion; and S2: performing image enhancement on the original surveillance video image training set to obtain enhanced images, and converting the enhanced images into sequence data. The pedestrian re-identification technology based on local feature attention uses a multi-head attention mechanism neural network to capture, extract video image feature sequences and replace convolution kernels in a convolutional neural network, uses fully connected layers and an activation function to combine local pedestrian feature sequences into complete pedestrian feature sequences through a weight matrix, performs prediction on the obtained pedestrian feature sequences, outputs position coordinates of pedestrians in the images and selects pedestrians to realize pedestrian re-identification.

One variation of a method for monitoring occupancy in a work area includes, at a sensor block: transitioning from an inactive state into an active state when an output of a motion sensor indicates motion in a work area; during a scan cycle in the active state, recording an image through an optical sensor at a time, detecting a set of humans in the image, detecting a second set of human effects in the image, predicting a second set of humans occupying but absent the work area based on the second set of human effects, and estimating a total occupancy in the work area at the time based on the set of humans and the second set of humans; and transmitting the total occupancy to a remote computer system for update of a scheduler for the work area.

Information for determining whether to evacuate or not is provided to a user by a computerized process. The process includes: acquiring a number of evacuees for each of a plurality of targets corresponding to a predetermined user; selecting a target of the plurality of targets based on the acquired numbers of evacuees for the plurality of targets and importance levels respectively set for the plurality of targets; and associating information on the selected target with information on the acquired number of evacuees for the selected target and outputting the information on the selected target and the information on the acquired number of evacuees for the selected target to the predetermined user.