Abstract: A system reinforcement learning method includes: processing an input image based on a first network of a system to obtain a first result; inputting the first result to a second network of the system to obtain a second result; and obtaining a reinforcement operation based on the second result by means of a reinforcement network, and adjusting the first result based on the reinforcement operation to obtain a target result. According to the embodiments of the present disclosure, information is fed back from downstream to upstream by means of the reinforcement network, and an output result of the system is optimized.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: acquiring an iris image group comprising at least two iris images to be compared; detecting iris locations in the iris images and segmentation results of iris areas in the iris images; performing multi-scale feature extraction and multi-scale feature fusion on an image area corresponding to the iris locations, to obtain iris feature maps corresponding to the iris images; performing comparison using the segmentation results and the iris feature maps respectively corresponding to the at least two iris images, and determining whether the at least two iris images correspond to the same object based on a comparison result of the comparison. Embodiments of the present disclosure realize accurate comparison of iris images.

Abstract: The present disclosure relates to methods and apparatuses for dynamic action detection and dynamic action control. A dynamic action detection method includes: adding an image frame in a video stream into a first queue, to obtain the first queue with a partially updated image frame; detecting a dynamic action in the partially updated image frame; and in response to the dynamic action not matching with the action detection result, updating an action detection result according to the dynamic action, the action detection result including an action reference result or a previously detected action detection result. A dynamic action detection in the present disclosure is executed according to the partially updated image frame in the first queue, so that the dynamic action can be detected more timely; and the dynamic action can also be rapidly determined, so that the action detection result is more accurate and the detection efficiency is higher.

Abstract: An image processing method and apparatus, and a storage medium are provided. The method includes: obtaining a first image and a second image of a to-be-authenticated object, where the first image is captured by a first camera module, and the second image is captured by at least one second camera module; comparing the first image with image data in a target library for identity authentication, to obtain a first authentication result; and in response to that the first authentication result is authentication failure, performing joint authentication on the first image and the second image, and determining the identity of the to-be-authenticated object according to a second authentication result of the joint authentication.

Abstract: A passenger state analysis method and apparatus, a vehicle, an electronic device and a storage medium. The method includes: obtaining a video stream of a rear seat area in a vehicle; performing face and/or body detection on at least one image frame in the video stream; determining state information of a passenger in the rear seat area according to a face and/or body detection result; and in response to the fact that the state information of the passenger satisfies a predetermined condition, outputting prompt information to a driver area or a specified device in the vehicle.

Abstract: Vehicle door unlocking control methods, apparatuses, and systems for vehicles. The vehicle door unlocking control method can be carried out by the apparatuses and systems and includes: obtaining an acquisition trigger signal; controlling, based on the acquisition trigger signal, a first camera assembly disposed on a vehicle to acquire a first face image of a user; performing feature matching between the first face image and a pre-stored second face image; performing living body detection on the first face image; and sending a vehicle door unlocking instruction to at least one vehicle door lock of the vehicle in response to successful feature matching and successful living body detection. This door unlocking solution may be used to provide face-based vehicle door opening in a safe, convenient and reliable manner.

Abstract: The embodiments of the present disclosure disclose a driving state analysis method. The driving state analysis method includes: performing fatigue state detection and distraction state detection for a driver on a driver image to obtain a fatigue state detection result and a distraction state detection result; in response to one of the fatigue state detection result and the distraction state detection result satisfying a predetermined alarm condition, outputting alarm information of a corresponding detection result that satisfies the predetermined alarm condition; and/or, in response to both the fatigue state detection result and the distraction state detection result satisfying the predetermined alarm condition, outputting alarm information of the fatigue state detection result that satisfies the predetermined alarm condition.

Abstract: A point cloud data fusion method includes: point cloud data collected respectively by a primary radar and each secondary radar arranged on a target vehicle is acquired, where the primary radar is one of radars on the target vehicle, and the secondary radar is a radar other than the primary radar among the radars on the target vehicle; a reflectivity in the point cloud data collected by the secondary radar is adjusted based on a pre-determined reflectivity calibration table of the secondary radar to obtain adjusted point cloud data of the secondary radar, where the reflectivity calibration table represents target reflectivity information of the primary radar, which matches each reflectivity corresponding to each scanning line of the secondary radar; and the point cloud data collected by the primary radar and the adjusted point cloud data corresponding to the secondary radar are fused to obtain fused point cloud data.

Abstract: The embodiments of the present disclosure disclose a driving state analysis method. The driving state analysis method includes: performing fatigue state detection and distraction state detection for a driver on a driver image to obtain a fatigue state detection result and a distraction state detection result; in response to one of the fatigue state detection result and the distraction state detection result satisfying a predetermined alarm condition, outputting alarm information of a corresponding detection result that satisfies the predetermined alarm condition; and/or, in response to both the fatigue state detection result and the distraction state detection result satisfying the predetermined alarm condition, outputting alarm information of the fatigue state detection result that satisfies the predetermined alarm condition.

Abstract: Provided are a task scheduling method and device, and a computer storage medium. The task scheduling method includes that: a dependency relationship among multiple operation tasks is determined according to operands corresponding to multiple operation tasks in an operation task queue; and the multiple operation tasks in the operation task queue are scheduled based on the dependency relationship among the multiple operation tasks.

Abstract: A method for determining road operation condition includes: acquiring an image of a road to be monitored; acquiring the number of vehicles on the road to be monitored, and dividing the road to be monitored into an upstream road and a downstream road based on the image and the number of vehicles on the road to be monitored; acquiring the number of vehicles on the upstream road and the number of vehicles on the downstream road, and determining a target domain value based on the number of vehicles on the road to be monitored, the number of vehicles on the upstream road, the number of vehicles on the downstream road, and a traffic wave of the road to be monitored; and determining that congestion occurs on the road to be monitored if the number of vehicles on the road to be monitored is greater than the target domain value.

Abstract: Embodiments of the present disclosure disclose a lane line-based intelligent driving control method and apparatus, and an electronic device. The method includes: obtaining a lane line detection result of a vehicle traveling environment; determining, according to a traveling state of the vehicle and the lane line detection result, an estimated distance of traveling out of the lane line by the vehicle and/or estimated time of traveling out of the lane line by the vehicle; and performing intelligent driving control on the vehicle according to the estimated distance and/or the estimated time. The embodiments of the present disclosure implement lane line-based intelligent control on the vehicle traveling state, thereby helping to improve the driving safety.

Abstract: Embodiments of the present disclosure provide a driving state detection method and apparatus, a driver monitoring system, and a vehicle. The driving state detection method includes: performing head pose detection and eye state detection on a driver image to obtain head pose information and eye state information; and determining detection results of fatigue state and distraction state of the driver according to the head pose information and the eye state information.

Abstract: Point cloud data collected by a radar device on a target vehicle can be acquired. Information on an obstacle within a set range from the target vehicle is determined based on the point cloud data. Radar blind zone information of the target vehicle is determined based on beam information of a beam transmitted by the radar device, as well as the information on the obstacle determined. The target vehicle is controlled according to the radar blind zone information of the target vehicle.

Abstract: A three-dimensional model generation method includes: acquiring first sphere position information of each first sphere of multiple first spheres in a camera coordinate system based on a first image including a first object, where the first spheres are configured to represent different parts of the first object respectively; generating a first rendered image based on the first sphere position information of the first spheres; obtaining gradient information of the first rendered image based on the first rendered image and a semantically segmented image of the first image; and adjusting the first sphere position information of the first spheres based on the gradient information of the first rendered image, and generating a three-dimensional model of the first object by utilizing the adjusted first sphere position information of the first spheres.

Abstract: A method for image labeling can include the following operations. An image to be labeled and a first scale indicator are acquired. A pixel neighborhood is constructed based on the first person point under the condition that the first scale indicator is more than or equal to a first threshold, the pixel neighborhood including a second pixel different from the first person point. A position of the second pixel is determined as the person point tag of the first person.

Abstract: A method for recognizing a dynamic gesture includes: positioning a dynamic gesture in a video stream to be detected to obtain a dynamic gesture box; capturing an image block corresponding to the dynamic gesture box from each of multiple image frames of the video stream; generating a detection sequence based on the captured image block; and performing dynamic gesture recognition according to the detection sequence.

Abstract: An image processing method includes: obtaining an image feature of each of a plurality of images for a same object; determining, according to the image feature of each of the plurality of images, a weight coefficient having a one-to-one correspondence with each image feature; and performing feature fusion processing on the image features of the plurality of images based on the weight coefficient of each image feature to obtain a fusion feature of the plurality of images.

Abstract: Embodiments of the present application provide an intelligent driving control method and apparatus, a vehicle, an electronic device, a medium, and product. The method includes: detecting a driving environment of a vehicle, to obtain a detection result of at least one type of target objects in the driving environment; determining whether the detection result meets a preset driving alert condition; in response to the detection result meeting the preset alert condition, determining whether a speed of the vehicle exceeds a speed threshold; and in response to the speed exceeding the speed threshold, triggering a driving alert according to the detection result.