Abstract:
An object detection system includes an image capture device, a memory, and a processor. The image capture device captures an image. The memory stores an instruction corresponding to an inference engine based on a multi-scale convolutional neural network architecture including a first, a second, and an object detection scale. The processor executes the instruction to: reduce network widths of convolution layers of the second scale; run the inference engine according to the adjusted convolutional neural network architecture to receive the image as an initial input; input a first output generated by the first scale according to the initial input into the second and the object detection scale; input a second output generated by the second scale according to the first output into the object detection scale; generate a final output according to the first and the second output by the object detection scale, to perform object detection on the image.
Abstract:
A voice control device includes a user database, a first image capturing module, a voice command module and a management module. The user database stores first user identification data of a first user account. The first image capturing module captures an environmental image. The voice command module is enabled to receive a voice command for controlling the voice control device. The management module is used to detect whether at least one facial image exists in the environmental image, and detect whether the facial image matches with the first user identification data, and when the facial image matches with the first user identification data, the management module logs in the first user account and enables the voice command module.
Abstract:
A driving assistance system and a driving assistance method are provided. The driving assistance system includes a plurality of image capturing devices, a congestion calculation module, and a determination module. The image capturing devices capture a plurality of regional images of a plurality of road sections of the target lane. The congestion calculation module calculates a plurality of congestion levels corresponding to the road sections according to the regional images. The determination module provides a lane changing message to the vehicle according to the congestion levels.
Abstract:
A smart parking management system and a smart parking management method are provided. The system includes a first smart pole, multiple second smart poles, and a processing device. An image capturing range of the first smart pole covers an entrance of a road section. An image capturing range of each second smart pole covers at least a parking space of the road section. The processing device is communicatively coupled to the first smart pole and the second smart poles. The processing device identifies first vehicle information of a first vehicle entering the road section according to an image stream captured by the first smart pole, obtains a movement trajectory of the first vehicle in the road section based on the first vehicle information and an image stream captured by each second smart pole, and determines where the first vehicle is parked according to the movement trajectory.
Abstract:
A face recognition system, a method for establishing data of face recognition, and a face recognizing method thereof are disclosed. The face recognition system includes an image obtaining device, a facial analysis module, and a feature comparison module. The image obtaining device is used to obtain a registered facial image. The facial analysis module is used to analyze the registered facial image to obtain a registered facial feature, so as to determine a feature threshold of the registered facial feature. The feature comparison module is used to compare the registered facial feature with a facial feature of a plural facial images to register a facial feature of a similar facial image corresponding to more than a similarity threshold as a false-positive facial image feature. Such that the facial analysis module determines a false-positive threshold of the false-positive facial image feature.
Abstract:
An object orientation identification method and an object orientation identification device are provided. The method is adapted for the object orientation identification device including a wireless signal transceiver. The object orientation identification device and a target object are both in a moving state. The method includes the following. A first signal is continuously transmitted by the wireless signal transceiver. A second signal reflected back from the target object is received by the wireless signal transceiver. Signal pre-processing is performed on the first signal and the second signal to obtain moving information of the target object with respect to the object orientation identification device. The moving information is input into a deep learning model to obtain orientation information of the target object with respect to the object orientation identification device. A relative orientation between the object orientation identification device and the target object is identified according to the orientation information.
Abstract:
A face recognition system and a method for enhancing face recognition are provided. The method includes: receiving a face image and obtaining a feature of the face image from a feature extraction model; registering the face image to set the feature of the face image as a first recognition feature; performing a synthesis operation on the face image according to at least one first adjustment parameter to generate a synthetic image, and obtaining a feature of the synthetic image from the feature extraction model; comparing first recognition feature with the feature of the synthetic image to obtain a feature similarity; comparing the feature similarity with a threshold value to obtain a comparison result; and registering the synthetic image when the comparison result indicates that the feature similarity is less than or equal to the threshold value.
Abstract:
An electronic device comprises a detecting unit and a processing unit, comprising. The method of adjusting execution state of electronic device comprises: a current environmental condition is detected through the detecting unit to generate a current environmental signal and the current environmental signal is transmitted to the processing unit. A current execution state of the electronic device is read through the processing unit. A step of comparing with a state look-up table of the electronic device is performed to allow the current environmental signal to correspond to a predetermined environmental condition in the state look-up table of the electronic device. It determines whether the current execution state of the electronic device conforms to a predetermined execution state of the predetermined environmental condition. If not, the current execution state of the electronic device is adjusted to allow the current execution state to be the same with the predetermined execution state.