Systems and methods for 3D image distification
摘要
說明書
In some embodiments, the 2D and 3D images input into the ensemble model are sets of images defining a “chunk” of images sharing a common timeframe, such as images 2D and 3D images taken at the same time for a movie. In some embodiments, a chunk classification can be determined for the common timeframe, where the chunk classification is based on one or more 2D3D image pair classifications of the 2D3D image pairs that make up the movie.
In other embodiments, the ensemble model can generate a confusion matrix that includes one or more 2D3D image pair classifications. The confusion matrix can be used for further analysis or review of the ensemble model, for example, to compare the accuracy of the model with other prediction models.
In some embodiments, the ensemble model may be used to generate a data structure series that can indicate one or more driver behaviors as determined from one or more 2D3D image pair classifications. The driver behaviors can be used to determine or develop a risk factor for a given driver. As mentioned herein, the driver behaviors can include any of left hand calling, right hand calling, left hand texting, right hand texting, eating, drinking, adjusting the radio, or reaching for the backseat.
Advantages will become more apparent to those of ordinary skill in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
