Abstract: The embodiments of the disclosure provide an intelligent glass and an intelligent window system, and relates to the technical field of window display. The intelligent glass of the disclosure includes a touch display assembly and a glass assembly. The touch display assembly is communicatively coupled to the glass assembly, and is configured to send a corresponding dimming instruction to the glass assembly based on a received touch instruction, such that the glass assembly adjusts its light transmittance based on the dimming instruction.

Abstract: Provided are an apparatus (1) for dispensing viscous fluid and a method for dispensing viscous fluid with the same. The apparatus (1) comprises: a support structure (2) adapted to receive a plurality of cartridges (41, 42); and a plurality of push rods (31, 32) and a transmission means (7) that are mounted to the support structure (2), each push rod being configured to be translatable in a longitudinal direction thereof. The transmission means (7) is configured to receive a driving force from a power source and transmit the driving force to the plurality of push rods (31, 32) at different transmission ratios so as to translate the plurality of push rods at different speeds.

Abstract: The present disclosure provides a dense crowd counting method and an apparatus, including: acquiring an image to be detected, where the image to be detected includes images of people; feeding the image to be detected into a convolutional neural network model to obtain a crowd density map of the image to be detected; and determining the number of the images of people in the image to be detected according to the crowd density map. Feature information of an image to be detected may be fully extracted through the above mentioned process, thereby realizing a better performance of crowd counting and density estimation, providing great convenience for subsequent security monitoring, crowd control and other applications.

Abstract: The present disclosure provides a target tracking method and device oriented to airborne-based monitoring scenarios. The method includes: obtaining a video to-be-tracked of the target object in real time; extracting a first frame and a second frame; trimming and capturing the first frame to derive an image for first interest region; trimming and capturing the second frame to derive an image for target template and an image for second interest region; inputting the image for target template and the image for first interest region into an appearance tracker network to derive an appearance tracking position; inputting the image for first interest region and the image for second interest region into a motion tracker network to derive a motion tracking position; and finally inputting the appearance tracking position and the motion tracking position into a deep integration network to derive a final tracking position.

Abstract: The present disclosure provides a dense crowd counting method and an apparatus, including: acquiring an image to be detected, where the image to be detected includes images of people; feeding the image to be detected into a convolutional neural network model to obtain a crowd density map of the image to be detected; and determining the number of the images of people in the image to be detected according to the crowd density map. Feature information of an image to be detected may be fully extracted through the above mentioned process, thereby realizing a better performance of crowd counting and density estimation, providing great convenience for subsequent security monitoring, crowd control and other applications.

Abstract: The present disclosure provides a target tracking method and device oriented to airborne-based monitoring scenarios. The method includes: obtaining a video to-be-tracked of the target object in real time; extracting a first frame and a second frame; trimming and capturing the first frame to derive an image for first interest region; trimming and capturing the second frame to derive an image for target template and an image for second interest region; inputting the image for target template and the image for first interest region into an appearance tracker network to derive an appearance tracking position; inputting the image for first interest region and the image for second interest region into a motion tracker network to derive a motion tracking position; and finally inputting the appearance tracking position and the motion tracking position into a deep integration network to derive a final tracking position.