A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The image data includes a first image that is temporally precedent to a second image. The processor is configured to process the image using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to output a video file in the video format.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The image data includes a first image that is temporally precedent to a second image. The processor is configured to process the image using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to output a video file in the video format.

A method for processing image frames, and an electronic device are provided. The method includes receiving image frames captured by an image sensor at a first frame rate and storing the received image frames in a memory. A change is detected in the stored image frames and an image frame is interpolated between the stored image frames after detecting the change. The interpolated image is stored in the memory.

A method for processing image frames, and an electronic device are provided. The method includes receiving image frames captured by an image sensor at a first frame rate and storing the received image frames in a memory. A change is detected in the stored image frames and an image frame is interpolated between the stored image frames after detecting the change. The interpolated image is stored in the memory.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The image data includes a first image that is temporally precedent to a second image. The processor is configured to process the image using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to output a video file in the video format.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The image data includes a first image that is temporally precedent to a second image. The processor is configured to process the image using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to output a video file in the video format.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The processor is configured to process the image data using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to store a video file in the video format.

A method and apparatus for night lapse video capture are disclosed. The apparatus includes an image sensor, a processor, a denoiser, and an encoder. The image sensor is configured to capture image data. The processor is configured to process the image data using an image processing pipeline. The denoiser is configured to perform denoising based on a motion estimation that is based on a comparison of a portion of the first image and a portion of the second image. The encoder is configured to encode the denoised image in a video format. The encoder is configured to store a video file in the video format.

A video processing apparatus includes a video analyzer that analyzes video data captured by a surveillance camera, detects an event belonging to a specific category, and outputs a detection result, a display controller that displays, together with a video of the video data, a category setting screen for setting a category of an event included in the video, and a learning data accumulator that accumulates, as learning data together with the video data, category information set in accordance with an operation by an operator to the category setting screen. The video analyzer performs learning processing by using the learning data accumulated in the learning data accumulator.