Abstract: Various systems and methods for for clustering facial images in, for example, surveillance systems.

Abstract: Systems and methods for joint feature extraction and quality prediction using a shared machine learning model backbone and a customized training dataset are provided. According to an embodiment, a computer system receives a training dataset including example images each labeled with a particular category of a set of categories, and trains a deep neural network (DNN) based on the training dataset to jointly perform for an input image (i) facial feature extraction in accordance with the facial feature extraction algorithm and (ii) a quality scoring in accordance with a quality prediction algorithm. In the embodiment, the DNN, once trained with the training dataset labeled using a custom labeling scheme is used for the facial feature extraction and the quality prediction. The facial feature extraction algorithm and the quality prediction algorithm share a common DNN backbone of the DNN.

Abstract: Various systems and methods for for clustering facial images in, for example, surveillance systems.

Abstract: Various embodiments provide systems and methods for discovering APIs for use in relation to network application security.

Abstract: Various systems and methods for for clustering facial images in, for example, surveillance systems.

Abstract: Various systems and methods for clustering facial images in, for example, surveillance systems.

Abstract: Various embodiments provide systems and methods for updating a training dataset so that the generated machine learning model can adapt to both short-term and long-term face variations including, for example, head pose, dressing, lighting conditions, and/or aging.

Abstract: Systems and methods for joint feature extraction and quality prediction using a shared machine learning model backbone and a customized training dataset are provided. According to an embodiment, a computer system receives a training dataset including example images each labeled with a particular category of a set of categories, and trains a deep neural network (DNN) based on the training dataset to jointly perform for an input image (i) facial feature extraction in accordance with the facial feature extraction algorithm and (ii) a quality scoring in accordance with a quality prediction algorithm. In the embodiment, the DNN, once trained with the training dataset labeled using a custom labeling scheme is used for the facial feature extraction and the quality prediction. The facial feature extraction algorithm and the quality prediction algorithm share a common DNN backbone of the DNN.

Abstract: Systems and methods for accelerated object detection in a surveillance system are provided. According to an embodiment, video frames captured by a camera are received by a processing resource of a surveillance system. Pixels of each video frame are partitioned into cells each representing a rectangular block of the pixels. The background cells within a particular video frame are estimated by comparing each of the cells of the particular video frame to a corresponding cell of other video frames. A number of ROIs within the particular video frame is detected by: (i) identifying active cells within the particular video frame based on the estimated background cells; and (ii) identifying the number of clusters of cells within the particular video frame by clustering the active cells. Then, object detection is caused to be performed within the number of ROIs by feeding the number of ROIs to a machine learning model.