An apparatus and a method with image recognition-based security are disclosed. The method includes, for an unlocked terminal, tracking a face detected in a previous frame, detecting a background region change between the previous frame and a current frame based on a region of the tracked face, when the background region change is not detected, determining whether a state maintenance time fails to meet a preset time, in response to the state maintenance time failing to meet the preset time, determining an operation mode to be a first operation mode for determining whether recognition succeeds for the current frame, performing the first operation mode, including performing face detection with respect to the current frame, and maintaining the unlocked state of the terminal for the current frame when the face is detected as a result of the performing of the face detection, representing that the recognition succeeded for the current frame.

An apparatus and a method with image recognition-based security are disclosed. The method includes, for an unlocked terminal, tracking a face detected in a previous frame, detecting a background region change between the previous frame and a current frame based on a region of the tracked face, when the background region change is not detected, determining whether a state maintenance time fails to meet a preset time, in response to the state maintenance time failing to meet the preset time, determining an operation mode to be a first operation mode for determining whether recognition succeeds for the current frame, performing the first operation mode, including performing face detection with respect to the current frame, and maintaining the unlocked state of the terminal for the current frame when the face is detected as a result of the performing of the face detection, representing that the recognition succeeded for the current frame.

A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.

A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.

A motion detection method includes providing a buffer including a first buffer associated with a background image and a second buffer associated with a foreground image; checking first similarity between the gray level of an input pixel and the first gray level of the first buffer; determining the input pixel as a still pixel if the first similarity is true; checking second similarity between the gray level and the second gray level of the second buffer; determining the input pixel as a moving pixel if the second similarity is false; determining the input pixel as the moving pixel if the second count value is less than the first count value; and determining the input pixel as the still pixel and swapping the first buffer with the second buffer, if the second count value is not less than the first count value.

A motion detection method includes providing a buffer including a first buffer associated with a background image and a second buffer associated with a foreground image; checking first similarity between the gray level of an input pixel and the first gray level of the first buffer; determining the input pixel as a still pixel if the first similarity is true; checking second similarity between the gray level and the second gray level of the second buffer; determining the input pixel as a moving pixel if the second similarity is false; determining the input pixel as the moving pixel if the second count value is less than the first count value; and determining the input pixel as the still pixel and swapping the first buffer with the second buffer, if the second count value is not less than the first count value.

The present disclosure describes a system which is able to detect and recognize wildlife, and in particular birds, using camera images. The present solution is comprised of algorithms, software and integrated hardware devices. Properly equipped, the system can be made to be portable and can be set up at any location for different wildlife detection and repelling purposes.

The present disclosure describes a system which is able to detect and recognize wildlife, and in particular birds, using camera images. The present solution is comprised of algorithms, software and integrated hardware devices. Properly equipped, the system can be made to be portable and can be set up at any location for different wildlife detection and repelling purposes.

Methods, systems, an apparatus, including computer programs encoded on a storage device, for training an image classifier. A method includes receiving an image that includes a depiction of an object; generating a set of poorly localized bounding boxes; and generating a set of accurately localized bounding boxes. The method includes training, at a first learning rate and using the poorly localized bounding boxes, an object classifier to classify the object; and training, at a second learning rate that is lower than the first learning rate, and using the accurately localized bounding boxes, the object classifier to classify the object. The method includes receiving a second image that includes a depiction of an object; and providing, to the trained object classifier, the second image. The method includes receiving an indication that the object classifier classified the object in the second image; and performing one or more actions.

Methods, systems, an apparatus, including computer programs encoded on a storage device, for training an image classifier. A method includes receiving an image that includes a depiction of an object; generating a set of poorly localized bounding boxes; and generating a set of accurately localized bounding boxes. The method includes training, at a first learning rate and using the poorly localized bounding boxes, an object classifier to classify the object; and training, at a second learning rate that is lower than the first learning rate, and using the accurately localized bounding boxes, the object classifier to classify the object. The method includes receiving a second image that includes a depiction of an object; and providing, to the trained object classifier, the second image. The method includes receiving an indication that the object classifier classified the object in the second image; and performing one or more actions.