Abstract: In some examples, systems and methods for object tracking are provided. For example, a method includes: receiving an image frame in a sequence of image frames; identifying an object of interest in the image frame using a single-object tracker (SOT) based upon one or more templates associated with the object of interest in a template repository; generating a SOT output based on the identified object of interest; and detecting one or more objects in the image frame using a multiple-object tracker (MOT). In some examples, the MOT including a machine-learning model. In some examples, the method further includes conducting a matching between the SOT output and each detected object of the one or more detected objects to generate a match result; and generating a tracker output based at least in part on the SOT output, the one or more detected objects, and the match result.

Abstract: In some examples, systems and methods for user-assisted object detection are provided. For example, a method includes: receiving a first image frame of a sequence of image frames, performing object detection using an object tracker to identify an object of interest in the first image frame, based upon one or more templates associated with the object of interest in a template repository, outputting a first indicator associated with a first image portion corresponding to the identified object of interest, and receiving a user input associated with the object of interest. In some examples, the user input indicates an identified image portion in the image frame. In some examples, the method further includes generating a retargeted template, based at least in part on the identified image portion, and determining a second image portion associated with the object of interest in a second image frame of the sequence of image frames using the object tracker, based at least in part on the retargeted template.

Abstract: Disclosed herein are systems and methods for sensor cueing. In one example, the method includes: receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object; generating a sensor command based at least in part upon the model inference, the sensor command comprising one or more object parameters associated with the target object and one or more sensor parameters associated with a sensor; and transmitting the sensor command to the sensor via a sensor API.

Abstract: Disclosed herein are systems and methods for model selection and update operation. The methods include receiving a model request with parameters selected from a group consisting of a type of computing model, a processing characteristic, and a data characteristic; receiving information associated with a plurality of computing models from a model repository; selecting one or more computing models based upon the model request; compiling a container request based on the model request and the one or more selected computing models; transmitting the container request to a container infrastructure; and coupling the one or more selected computing models to an artificial intelligence inference platform (AIP).