Abstract: Training data generated at a particular device deployed in a machine-to-machine network is used to train a first plurality of decision trees for inclusion in a random forest model for use in anomaly detection. Copies of the trained first plurality of decision trees are sent to at least one other device deployed in the machine-to-machine network and copies of a second plurality of decision trees are received from the other device, which were trained at the other device using training data generated by the other device. The random forest model is generated to include the first and second plurality of decision trees. The random forest model is used by the particular device to detect anomalies in data generated by the particular device.

Abstract: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

Abstract: In embodiments, one or more non-transitory computer-readable storage media comprise a set of instructions, which, when executed on a processor of a server, causes the server to receive sensor data from at least one sensor proximate to an entity, the entity is a human under care of at least one temporary guardian (TG) pursuant to a set of guardianship rules, the guardianship rules including a pre-defined geographic boundary in which the entity is to remain while under the care of the at least one TG. When executed, the instructions further cause the server to extract location metadata of the entity from the sensor data, and based at least in part on the metadata, send notifications to the TG and to a primary guardian (PG) of the entity when the entity is outside of the pre-defined boundary.

Abstract: A system includes a self-learning module for creating a self-learned planogram based on images of shelving units at a location and shelving unit tracking. The self-learned planogram includes shelving unit locations for the shelving units. The system also includes a training module for training the merchandise tracking model based on merchandise-shelving unit clustering. The merchandise-shelving unit clustering is based on the self-learned planogram and sensor readings received from sensors at the location. The sensor readings are associated with items at the location. The system further includes a tracking module for tracking and storing locations of the items based on the sensor readings and the merchandise tracking model. The system also includes a planogram compliance module for determining planogram compliance based on comparing the self-learned planogram to the item locations.

Abstract: An embodiment of a semiconductor package apparatus may include technology to determine if a user is proximate to a display, determine a characteristic of the user, display a message on the display based on the characteristic of the user, and complete a transaction with the user proximate to the display. Non-limiting examples of environments for some embodiments may include any place where informational messaging is directed at people and where it may be beneficial to adjust such messaging to complete a transaction with the user. Other embodiments are disclosed and claimed.

Abstract: In one embodiment, an apparatus may comprise a sensor to detect a plurality of radio signals from one or more transmitters. The apparatus may further comprise a processor to: identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; and detect one or more human-asset interactions based on the plurality of signal characteristics.

Abstract: An embodiment of a semiconductor package apparatus may include technology to associate an asset to a fixture with a device positioned proximate to the fixture, and determine a location of the fixture based on a location of the device. Other embodiments are disclosed and claimed. Non-limiting example applications may include shipping, logistics, warehouse asset tracking, retail, etc.

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: Monitoring and analyzing people interacting with an environment, e.g., looking around, touching, talking, exclaiming, moving items around, staring at something, walking, as well as producing identifiable physiological and/or movement that may be analyzed to determine a person's interest and/or emotional response to the environment. In a shopping context, analysis may determine customer interest in a specific product. Interest/emotional response may be translated into a quantified review and the reviews may be pushed peer-to-peer to others in the environment, used to update signage, propagated to social media, etc. Reviews may be determined in real time and if related to an unusual or negative review, may trigger one or more various responses, e.g., automatic assistance to clear up a problem, perform other error handling, notify a vendor of a problem, trigger an update such as to lower prices, etc.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: Localization data is accessed, which is based on information collected by one or more sensor devices deployed within an environment. The localization data identifies presence of a person within a particular location within the environment. A feature vector is access, which includes values to describe a plurality of different physical characteristics of the person as measured by a set of one or more sensor devices deployed within the environment when the person was present within the particular location. An emotion of the person is determined from the feature vector data, and the emotion is associated with the particular location within the environment. An emotion heat map is generated of the environment to map emotional responses determined within the environment to specific locations within the environment.

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory stores visual data captured by one or more sensors. The processor detects one or more first objects in the visual data based on a machine learning model and one or more first reference templates. The processor further determines, based on an object ontology, that the visual data is expected to contain a second object, wherein the object ontology indicates that the second object is related to the one or more first objects. The processor further detects the second object in the visual data based on the machine learning model and a second reference template. The processor further determines, based on an inference rule, that the visual data is expected to contain a third object. The processor further detects the third object in the visual data based on the machine learning model and a third reference template.

Abstract: Systems and methods for double real-time bidding at a brick and mortar store are disclosed. The systems may include a shopper tracking system for tracking a plurality of shoppers, an electronic sign for displaying messages offering products for auction, and a real-time bidding system for receiving bids to sell an offered product from a plurality of sellers and bids to purchase the product from the plurality of shoppers. Shopping behavior data may be collected by the shopper tracking system from the plurality of shoppers, and used by the real-time bidding system to select the messages offering products.

Abstract: Techniques are provided for adaptive distribution of video analysis workload over a network of video processor nodes. The nodes may include, for example, internet protocol (IP) cameras, video recorders and/or data centers. The network may also include a management system configured to assign video analysis tasks to the nodes based on the node resources and predictive modelling of the node workload. The management system may re-distribute the tasks based on performance monitoring. Some assigned tasks may be bound to the node while other tasks may be transferrable, by the node, to other nodes. The nodes may be configured to determine which of the transferrable tasks will be locally executed or transferred based on a check of resource usage against a usage policy that specifies thresholds for the determinations. The nodes may be configured to transmit video analysis packets, including image data, analysis completion status and analysis results, to other nodes.

Abstract: In one embodiment, an apparatus comprises a top-view sensing device to capture sensor data associated with an environment below the top-view sensing device. The apparatus further comprises a processor to: obtain the sensor data captured by the top-view sensing device; generate, based on the sensor data, a visual representation of the environment below the top-view sensing device; determine that the visual representation comprises a representation of a person; identify one or more features associated with the representation of the person; and identify demographic information associated with the person based on the one or more features.

Abstract: In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph.

Abstract: Multiple devices are detected in an environment and a user input is received to define a relationship between two or more devices in the plurality of devices. A system can determine that a first of the two or more devices includes a sensor resource and a second of the two or more devices includes an actuator resource. Data is identified describing outputs of the first device corresponding to the sensor resource and inputs of the second device corresponding to the actuator resource. A model is generated modeling interoperation of the sensor resource and actuator resource based at least in part on the data.