Abstract: A predictive security camera system includes a camera, an object recognizer for detecting an object in a current frame of the camera, a convolutional encoder for generating a feature representation of the current frame, a primary clustering module for clustering the feature representation in primary clusters of the detected object, a vector generation module for generating an information vector that includes a set of variables such as primary class, sub-class, and one or more external factors associated with the feature representation, an anomaly clustering module for clustering each information vector into one or more secondary clusters wherein each variable in the information vector is a clustering dimension, and a predictive analysis module for receiving at least one variable of an information vector as an input, and predicting at least one another variable of the information vector based on one or more previous secondary clusters formed by the anomaly clustering module.

Abstract: A method for performing real-time recognition of objects includes receiving an input video stream from a camera, pre-processing a current frame of the input video stream using one or more pre-processing layers of a neural network structure, detecting if there is an object in the current pre-processed frame using an auxiliary branch of the neural network structure, recognizing one or more objects in the current pre-processed frame using a primary branch of the neural network structure if an object is detected in the current pre-processed frame, and displaying the one or more recognized objects of the current frame in one or more bounding boxes.

Abstract: A method for performing real-time recognition of objects in motion includes receiving an input video stream from a camera, generating one or more depth maps for one or more frames of the input video stream, recognizing one or more objects in a current frame based on corresponding depth map using a machine learning algorithm, and displaying the one or more recognized objects in the current frame in one or more bounding boxes.

Abstract: A method for detecting temporal pattern anomalies in a video stream includes detecting an object in a current frame of the video stream, generating a processed current frame that contains the detected object, generating a feature representation of the processed current frame, clustering the feature representation in one or more primary clusters in a clustering space of the primary class, generating an information vector of the feature representation, that includes information regarding the primary class, the sub-class and one or more external factors associated with the feature representation, clustering each information vector into one or more secondary clusters, and reporting a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of a feature presentation of a previous frame.

Abstract: A method for detecting temporal pattern anomalies in a video stream includes detecting an object in a current frame of the video stream, generating a processed current frame that contains the detected object, generating a feature representation of the processed current frame, clustering the feature representation in one or more primary clusters in a clustering space of the primary class, generating an information vector of the feature representation, that includes information regarding the primary class, the sub-class and one or more external factors associated with the feature representation, clustering each information vector into one or more secondary clusters, and reporting a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of a feature presentation of a previous frame.

Abstract: A predictive security camera system includes a camera, an object recognizer for detecting an object in a current frame of the camera, a convolutional encoder for generating a feature representation of the current frame, a primary clustering module for clustering the feature representation in primary clusters of the detected object, a vector generation module for generating an information vector that includes a set of variables such as primary class, sub-class, and one or more external factors associated with the feature representation, an anomaly clustering module for clustering each information vector into one or more secondary clusters wherein each variable in the information vector is a clustering dimension, and a predictive analysis module for receiving at least one variable of an information vector as an input, and predicting at least one another variable of the information vector based on one or more previous secondary clusters formed by the anomaly clustering module.

Abstract: A method for performing real-time detection of objects in motion includes receiving an input video stream from a camera, detecting if a motion has occurred in a current frame of the input video stream, providing the current frame for object detection if the motion has been detected therein, detecting a moving object in the current frame, displaying the detected moving object, simultaneously tracking a location of the detected moving object within corresponding frame, while the object detection continues for one or more moving objects, and generating a tracking box and overlaying the tracking box on the detected moving object and then transmitting the video to the display, and continuing the tracking of the detected moving object till the object detection continues for corresponding one or more moving objects.

Abstract: A method for performing real-time recognition of objects includes receiving an input video stream from a camera, pre-processing a current frame of the input video stream using one or more pre-processing layers of a neural network structure, detecting if there is an object in the current pre-processed frame using an auxiliary branch of the neural network structure, recognizing one or more objects in the current pre-processed frame using a primary branch of the neural network structure if an object is detected in the current pre-processed frame, and displaying the one or more recognized objects of the current frame in one or more bounding boxes.

Abstract: A method for performing real-time recognition of objects in motion includes receiving an input video stream from a camera, generating one or more depth maps for one or more frames of the input video stream, recognizing one or more objects in a current frame based on corresponding depth map using a machine learning algorithm, and displaying the one or more recognized objects in the current frame in one or more bounding boxes.

Abstract: A method for performing real-time detection of objects in motion includes receiving an input video stream from a camera, detecting if a motion has occurred in a current frame of the input video stream, providing the current frame for object detection if the motion has been detected therein, detecting a moving object in the current frame, displaying the detected moving object, simultaneously tracking a location of the detected moving object within corresponding frame, while the object detection continues for one or more moving objects, and generating a tracking box and overlaying the tracking box on the detected moving object and then transmitting the video to the display, and continuing the tracking of the detected moving object till the object detection continues for corresponding one or more moving objects.