Abstract: The present invention extends to methods, systems, and computer program products for profiling video output devices. A frameset is received from a video output device. A first characteristic of the frameset is profiled. A focus area within the frameset is identified based on the profile of the first characteristic. A second characteristic is profiled within the focus area. A baseline output profile for the video device is generated based at least on the profile of the second characteristic. The baseline output profile is stored in repository.

Abstract: The present invention extends to methods, systems, and computer program products for classifying video. In one aspect, a model is trained to classify video. A reference model capable of identifying signals from a plurality of signals classes is used along with tagged training data to train a signal class specific model. The signal class specific model uses less resources to identify the specific signal class model relative to the reference model. The signal class specific model can be deployed to an event detection infrastructure or other device. Frames from video streams can be classified using the signal class specific model.

Abstract: The present invention extends to methods, systems, and computer program products for assessing video stream quality. The quality of a video stream is classified into one of a plurality of quality classifications including a low quality classification and at least one other higher quality classification. A plurality of video quality thresholds are accessed. If any one of the plurality of video quality thresholds is not satisfied, the video stream is classified as low quality. The characteristics of a video stream frame are computed to satisfy each of the plurality of quality thresholds. A video stream technical score is computed from content of the frame based on and subsequent to satisfaction of the plurality of quality thresholds. The video stream is classified as a specified quality, from among the plurality of quality classifications, based on the video stream technical score.

Abstract: The present invention extends to methods, systems, and computer program products for estimating color of vehicles on a roadway. The color can be at least estimated from captured imagery, such as, frames of a video stream. The captured imagery can be captured by variably configured traffic cameras, other public cameras, private cameras (e.g., CCTV), or virtually any other camera, etc., which may have different operating conditions. Color confidence scores can be derived based on image characteristics.

Abstract: The present invention extends to methods, systems, and computer program products for classifying video. In one aspect, a model is trained to classify video. A reference model capable of identifying signals from a plurality of signals classes is used along with tagged training data to train a signal class specific model. The signal class specific model uses less resources to identify the specific signal class model relative to the reference model. The signal class specific model can be deployed to an event detection infrastructure or other device. Frames from video streams can be classified using the signal class specific model.

Abstract: The present invention extends to methods, systems, and computer program products for estimating color of vehicles on a roadway. The color can be at least estimated from captured imagery, such as, frames of a video stream. The captured imagery can be captured by variably configured traffic cameras, other public cameras, private cameras (e.g., CCTV), or virtually any other camera, etc., which may have different operating conditions.

Abstract: The present invention extends to methods, systems, and computer program products for estimating color of vehicles on a roadway. The color can be at least estimated from captured imagery, such as, frames of a video stream. The captured imagery can be captured by variably configured traffic cameras, other public cameras, private cameras (e.g., CCTV), or virtually any other camera, etc., which may have different operating conditions. Color confidence scores can be derived based on image characteristics.