Abstract: An aerial vehicle is configured to operate within indoor spaces. The aerial vehicle is programmed with positions of waypoints within three-dimensional space, and to calculate a trajectory for traveling through such waypoints in a manner that minimizes snap of the aerial vehicle. Where a distance between a pair of the waypoints is sufficiently long, the aerial vehicle inserts intervening waypoints for planning purposes, and programs the aerial vehicle to travel at a maximum speed between the intervening waypoints. Upon detecting an obstacle with a first range using one or more sensors, the aerial vehicle reduces its speed and monitors a second, shorter range using the sensors, and compensates for motion between such readings.

Abstract: A network congestion handling method is provided. The method is applied to an HPC cluster including a plurality of hosts, and the plurality of hosts are connected via a network device. A transmit end obtains a first acknowledgment message including a network congestion scenario identifier. The network congestion scenario identifier is an identifier indicating a scenario in which network congestion occurs in a process in which the transmit end transmits data to a receive end, and the transmit end and the receive end are any two hosts in the plurality of hosts. Then, the transmit end adjusts a data sending rate of the transmit end based on the network congestion scenario identifier.

Abstract: Various embodiments enable a primary user to be identified and tracked using stereo association and multiple tracking algorithms. For example, a face detection algorithm can be run on each image captured by a respective camera independently. Stereo association can be performed to match faces between cameras. If the faces are matched and a primary user is determined, a face pair is created and used as the first data point in memory for initializing object tracking. Further, features of a user's face can be extracted and the change in position of these features between images can determine what tracking method will be used for that particular frame.

Abstract: Various embodiments enable a primary user to be identified and tracked using stereo association and multiple tracking algorithms. For example, a face detection algorithm can be run on each image captured by a respective camera independently. Stereo association can be performed to match faces between cameras. If the faces are matched and a primary user is determined, a face pair is created and used as the first data point in memory for initializing object tracking. Further, features of a user's face can be extracted and the change in position of these features between images can determine what tracking method will be used for that particular frame.

Abstract: Various embodiments enable a primary user to be identified and tracked using stereo association and multiple tracking algorithms. For example, a face detection algorithm can be run on each image captured by a respective camera independently. Stereo association can be performed to match faces between cameras. If the faces are matched and a primary user is determined, a face pair is created and used as the first data point in memory for initializing object tracking. Further, features of a user's face can be extracted and the change in position of these features between images can determine what tracking method will be used for that particular frame.

Abstract: Object tracking, such as may involve face tracking, can utilize different detection templates that can be trained using different data. A computing device can determine state information, such as the orientation of the device, an active illumination, or an active camera to select an appropriate template for detecting an object, such as a face, in a captured image. Information about the object, such as the age range or gender of a person, can also be used, if available, to select an appropriate template. In some embodiments instances of templates can be used to process various orientations, while in other embodiments specific orientations, such as upside down orientations, may not be processed for reasons such as rate of inaccuracies or infrequency of use for the corresponding additional resource overhead.