Automatic composition of video with dynamic background and composite frames selected based on frame and foreground object criteria
A processing device generates composite images from a sequence of images. The composite images may be used as frames of video. A foreground/background segmentation is performed at selected frames to extract a plurality of foreground object images depicting a foreground object at different locations as it moves across a scene. The foreground object images are stored to a foreground object list. The foreground object images in the foreground object list are overlaid onto subsequent video frames that follow the respective frames from which they were extracted, thereby generating a composite video.
1. A method for generating a composite output video from an input video having a sequence of frames, the method comprising:
selecting from the sequence of frames, a range of frames for processing; training a predictive model based on a plurality of training video frames, the predictive model determining whether a pixel in a given video frame belongs to a background model or foreground object;
performing, by a processing device, a foreground/background segmentation on each of the frames in the range of frames to extract a plurality of candidate foreground object images based on the predictive model, each of the candidate foreground object images comprising a representation of the foreground object depicted in a corresponding video frame with background pixels subtracted;
selecting, based on an image metric, a selected foreground object image from the plurality of candidate foreground object images;
storing the selected foreground object image to a foreground object list;
overlaying the stored foreground object image in the foreground object list on a current video frame to generate a composite video frame;
determining if a frame number of the current video frame is a multiple of a predefined integer X and responsive to the frame number of the current video frame being the multiple of the predefined integer X, updating the predictive model.
2. The method of claim 1 , wherein selecting the selected foreground object image comprises:
determining an image quality metric for each of the candidate foreground object images; and
determining that the selected foreground object image has a highest quality metric.
3. The method of claim 1 , wherein selecting the selected foreground object image comprises:
determining a face detection likelihood on each of the candidate foreground object images; and
determining that the selected foreground object image has a highest face detection likelihood.
4. The method of claim 1 , wherein selecting the selected foreground object image comprises:
determining a motion parameter for each of the candidate foreground object images; and
determining that the selected foreground object image has a motion parameter best matching a predefined motion criteria.
5. The method of claim 1 , wherein performing the foreground/background segmentation comprises:
obtaining a preliminary foreground object image;
applying a filter to reduce noise in the preliminary foreground object image to generate a filtered image;
detecting a filled convex hull region in the preliminary foreground object image;
adding extra pixels from the filtered image to the preliminary foreground object image to generate a temporary image;
discarding pixels in the temporary image outside the filled convex hull region to generate a noisy convex hull image; and
closing gaps in foreground regions of the noisy convex hull image to generate the foreground object image.
6. The method of claim 1 , wherein the predictive model comprises an adaptive Gaussian Mixture Model.
7. A non-transitory computer-readable storage medium storing instructions for generating a composite output video from an input video having a sequence of frames, the instructions when executed by a processor causing the processor to perform steps comprising:
selecting from the sequence of frames, a range of frames for processing;
performing a foreground/background segmentation on each of the frames in the range of frames to extract a plurality of candidate foreground object images based on a predictive model, each of the candidate foreground object images comprising a representation of a foreground object depicted in a corresponding video frame with background pixels subtracted;
selecting, based on an image metric, a selected foreground object image from the plurality of candidate foreground object images;
storing the selected foreground object image to a foreground object list; and
overlaying the stored foreground object image in the foreground object list on a current video frame to generate a composite video frame;
wherein the performing of the foreground/background segmentation comprises:
obtaining a preliminary foreground object image;
applying a filter to reduce noise in the preliminary foreground object image to generate a filtered image;
detecting a filled convex hull region in the preliminary foreground object image;
adding extra pixels from the filtered image to the preliminary foreground object image to generate a temporary image;
discarding pixels in the temporary image outside the filled convex hull region to generate a noisy convex hull image; and
closing gaps in foreground regions of the noisy convex hull image to generate the foreground object image.
8. The non-transitory computer-readable storage medium of claim 7 , wherein selecting the selected foreground object image comprises:
determining an image quality metric for each of the candidate foreground object images; and
determining that the selected foreground object image has a highest quality metric.
9. The non-transitory computer-readable storage medium of claim 7 , wherein selecting the selected foreground object image comprises:
determining a face detection likelihood on each of the candidate foreground object images; and
determining that the selected foreground object image has a highest face detection likelihood.
10. The non-transitory computer-readable storage medium of claim 7 , wherein selecting the selected foreground object image comprises:
determining a motion parameter for each of the candidate foreground object images; and
determining that the selected foreground object image has a motion parameter best matching a predefined motion criteria.
11. The non-transitory computer-readable storage medium of claim 7 , wherein the instructions when executed further cause the processor to perform a step of:
training the predictive model based on a plurality of training video frames, the predictive model to predict whether a pixel in a given video frame belongs to a background model or the foreground object.
12. The non-transitory computer-readable storage medium of claim 11 , wherein the instructions when executed further cause the processor to perform the steps of:
determining if a frame number of the current video frame is a multiple of a predefined integer X; and
responsive to the frame number of the current video frame being the multiple of the predefined integer X, updating the predictive model.
13. The non-transitory computer-readable storage medium of claim 7 , wherein the predictive model comprises an adaptive Gaussian Mixture Model.
14. A camera apparatus comprising:
one or more processor apparatus; and
a non-transitory computer-readable storage medium configured to store instructions for generating a composite output video from an input video having a sequence of frames, the instructions being configured to, when executed by the one or more processor apparatus, cause the camera apparatus to:
select from the sequence of frames, a range of frames for processing;
perform a foreground/background segmentation on each of the frames in the range of frames to extract a plurality of candidate foreground object images based on a predictive model, each of the candidate foreground object images comprising a representation of a foreground object depicted in a corresponding video frame with background pixels subtracted;
select, based on an image metric, a selected foreground object image from the plurality of candidate foreground object images;
store the selected foreground object image to a foreground object list;
overlay the stored foreground object image in the foreground object list on a current video frame to generate a composite video frame;
determine if a frame number of the current video frame is a multiple of a predefined integer X; and
responsive to the frame number of the current video frame being the multiple of the predefined integer X, update the predictive model.
15. The camera apparatus of claim 14 , wherein the selection of the selected foreground object image comprises:
determination of an image quality metric for each of the candidate foreground object images; and
determination that the selected foreground object image has a highest quality metric.
16. The camera apparatus of claim 14 , wherein the selection of the selected foreground object image comprises:
determination of a face detection likelihood on each of the candidate foreground object images; and
determination that the selected foreground object image has a highest face detection likelihood.
17. The camera apparatus of claim 14 , further comprising wherein the selection of the selected foreground object image comprises:
determination of a motion parameter for each of the candidate foreground object images; and
determination that the selected foreground object image has a motion parameter best matching a predefined motion criteria.
18. The camera apparatus of claim 14 , wherein the instructions that cause the camera apparatus to perform the foreground/background segmentation further comprise instructions configured to cause the camera apparatus to:
obtain a preliminary foreground object image;
apply a filter to reduce noise in the preliminary foreground object image to generate a filtered image;
detect a filled convex hull region in the preliminary foreground object image;
add extra pixels from the filtered image to the preliminary foreground object image to generate a temporary image;
discard pixels in the temporary image outside the filled convex hull region to generate a noisy convex hull image; and
close gaps in foreground regions of the noisy convex hull image to generate the foreground object image.
19. The camera apparatus of claim 14 , wherein the instructions are further configured to, when executed by the one or more processor apparatus, cause the camera apparatus to train the predictive model based on a plurality of training video frames, the predictive model configured to predict whether a pixel in a given video frame belongs to a background model or the foreground object.
20. The camera apparatus of claim 14 , wherein the camera apparatus is configured to be in data communication with a network, the network comprising the camera apparatus and one or more of a server entity and a client device;
wherein the client device comprises a user device configured to receive inputs from a user, and transmit and receive data via the network; and
wherein the server entity is configured to store multimedia data captured by the camera apparatus, and provide a user interface for interaction by a user.