Abstract: Systems, methods, apparatuses and non-transitory computer executable media configured to unify preprocessing, configuration, training, monitoring, and evaluation of multiple neural network based object detection algorithms under a singular development environment/platform (i.e., a “unified training platform”). The unified training platform may include a neural network agnostic model training environment that addresses the deficiencies described above and may allow for unified data annotation formatting. In addition to incorporating a wide variety of state-of-the-art neural networks into the unified training platform, the unified training platform may also provide full accessibility to available network optimizations. The present disclosure may also include a universal model converter.

Abstract: A machine learning (“ML”) model may be used to detect a presence of an object in one or more frames received from a camera sensor. The ML model may insert bounding boxes around the object and annotate the bounding boxes with one or more attributes of the object. The one or more frames and the annotated bounding boxes may be stored in a database configured to be searchable by at least one attribute of the one or more attributes. It may be determined whether the object is true positive (“TP”) event or a false positive (“FP”) event. The ML model may be re-trained using one or more of the database and the determination. If the object is a TP event, an alert may be transmitted to one or more devices with a location of the object that is based off of location information received from the camera sensor.