Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for presenting a first application's interface within a second application's interface. One of the methods includes receiving, by an active application executing on a user device that is not an application store, digital content referencing a second application not installed on the user device; obtaining application store data for the second application; providing, a user interface of the active application, the digital content with an application store user interface that includes (i) the application store data for the second application and (ii) an install element for triggering installation of the second application on the user device from within the active application; detecting user interaction with the install element; and in response to detecting the user interaction with the install element, triggering a download of the second application independent of launching an application for the application store.

Abstract: A RISC-V branch prediction method and device, an electronic device and a computer readable storage medium are provided. On the basis of the prior art, the remaining jump times of the jump instruction are additionally acquired, and the single jump step length (the single jump step length is not fixed to be 1) is calculated according to the difference of remaining jump times during two consecutive jumps, whether the target jump instruction has executed the last jump can be judged according to the single jump step length of a jump instruction and in combination with the real-time remaining jump times, so as to determine the jump times that need to be executed subsequently according to the judgment result.

Abstract: An object tracking device includes a short-term processing portion and a long short-term processing portion that are implemented by circuitry and work in a collaborative manner to track an object. The short-term processing portion includes a filter that tracks the object based on short-term memory and spatiotemporal consistency. The long short-term processing portion performs key-point matching-tracking and estimation based on a key-point database in order to track the object. A controller determines an output of the object tracking device based on the processing conducted by the short-term and long short-term processing portions of the tracking device, respectively.

Abstract: An object tracking device includes a short-term processing portion and a long short-term processing portion that are implemented by circuitry and work in a collaborative manner to track an object. The short-term processing portion includes a filter that tracks the object based on short-term memory and spatiotemporal consistency. The long short-term processing portion performs key-point matching-tracking and estimation based on a key-point database in order to track the object. A controller determines an output of the object tracking device based on the processing conducted by the short-term and long short-term processing portions of the tracking device, respectively.

Abstract: An object tracking device includes a short-term processing portion and a long short-term processing portion that are implemented by circuitry and work in a collaborative manner to track an object. The short-term processing portion includes a filter that tracks the object based on short-term memory and spatiotemporal consistency. The long short-term processing portion performs key-point matching-tracking and estimation based on a key-point database in order to track the object. A controller determines an output of the object tracking device based on the processing conducted by the short-term and long short-term processing portions of the tracking device, respectively.

Abstract: A method of tracking a target object in frames of video data includes receiving a first tracking position associated with the target object in a first frame of a video sequence; identifying, for a second frame of the video sequence, a plurality of representation levels and at least one node for each representation level; determining, by a processor, a second tracking position in the second frame by estimating motion of the target object in the second frame between the first frame and the second frame; determining, at each representation level by the processor, a value for each node based on a conditional property of the node in the second frame; and adjusting, by the processor, the second tracking position based on the values determined for each of the nodes and interactions between at least some of the nodes at different representation levels.

Abstract: An object tracking device includes a short-term processing portion and a long short-term processing portion that are implemented by circuitry and work in a collaborative manner to track an object. The short-term processing portion includes a filter that tracks the object based on short-term memory and spatiotemporal consistency. The long short-term processing portion performs key-point matching-tracking and estimation based on a key-point database in order to track the object. A controller determines an output of the object tracking device based on the processing conducted by the short-term and long short-term processing portions of the tracking device, respectively.

Abstract: Tracking object in frames of video data, including receiving a first tracking position associated with the target object in a first frame of a video sequence; identifying, for a second frame of the video sequence, a plurality of representation levels and at least one node for each representation level; determining, by a processor, a second tracking position in the second frame by estimating motion of the target object in the second frame between the first frame and the second frame; determining, at each representation level by the processor, a value for each node based on a conditional property of the node in the second frame; and adjusting, by the processor, the second tracking position based on the values determined for each of the nodes and interactions between at least some of the nodes at different representation levels.