Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: The present invention relates to pick planning for robotic picking applications to improve efficiency of automated picking operations and reduce robot down time. A pick plan is computed by obtaining data of a pick scene, processing the obtained data to identify objects and determine features associated with the objects, and determining an order and pick instructions based on the features. A computed pick plan may be periodically verified by reacquiring data of the pick scene and comparing the reacquired data with previous pick scene data in order to determine if a pick plan remains appropriate or should be updated or discarded and recomputed.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: The present disclosure describes various embodiments of surveillance systems and methods. In one such embodiment, an exemplary surveillance system includes at least one video camera configured to capture video data of a surveilled area; and a computing device that stores a surveillance program. An exemplary surveillance program includes computer-executable instructions configured to: analyze the video data captured by the at least one video camera; identify objects that enter the surveilled area and log a time at which the objects entered the surveilled area; determine an object type for each object; track the identified objects to determine a period of time the objects have been present within the surveilled area; and generate and transmit an alert for each identified object that has been present within the surveilled area for a period of time that exceeds a predetermined time threshold.

Abstract: The present disclosure describes various embodiments of surveillance systems and methods. In one such embodiment, an exemplary surveillance system includes at least one video camera configured to capture video data of a surveilled area; and a computing device that stores a surveillance program. An exemplary surveillance program includes computer-executable instructions configured to: analyze the video data captured by the at least one video camera; identify objects that enter the surveilled area and log a time at which the objects entered the surveilled area; determine an object type for each object; track the identified objects to determine a period of time the objects have been present within the surveilled area; and generate and transmit an alert for each identified object that has been present within the surveilled area for a period of time that exceeds a predetermined time threshold.

Abstract: A method and system for intercepting and forwarding High-Speed SECS Message Services (HSMS) communication between at least two entities, includes a fail-safe bypass to ensure the communications link between the entities is not severed upon failure of the intercepting/forwarding agent. A “pass-through” agent is placed in between two entities communicating via an HSMS link, such that the pass-through agent is able to intercept messages from one entity and forward it to the other entity, and vice versa. The pass-through agent is able to see all messages between the two entities, and is also able to create HSMS messages and send them to one of the entities as if the message had come from the other entity, thereby conferring the ability to inject additional HSMS messages. Should the pass-through agent fail, a bypass mechanism ensures that the two entities can automatically resume HSMS communication without the pass-through agent.

Abstract: Embodiments of the present invention relate to managing timestamps associated with received data. According to one embodiment, data is collected from a device that generates data at a specified rate, but which lacks a built-in clock. An accurate timestamp is assigned to the data by first taking an absolute timestamp from a reference clock, and then adding a calculated amount of time to each subsequent data point based on an estimate of the sampling frequency of the device. As the generated timestamp drifts from the actual reference clock time, the sampling frequency is re-estimated based on the amount of detected drift.