Nvidia Corporation

Joonhwa Shin

Kaustubh Purandare

Zheng Liu

Fangyu Li

Bhanu Nagendra Pisupati

Rohit Ramesh Vaswani

David Ung

Rahul Maruti Bhagwat

Apparatuses, systems, and techniques for real-time persistent object tracking for intelligent video analytics systems. A state of a first object included in an environment may be tracked based on a first set of images depicting the environment. The first set of images may be generated during a first time period. It may be determined that the first object is not detected in the environment depicted in a second set of images. The second set of images may be generated during a second time period that is subsequent to the first time period. One or more predicted future states of the first object may be obtained in view of the state of the first object in the environment depicted in the first set of images. A second object may be detected in the environment depicted in a third set of images generated during a third time period that is subsequent to the second time period. A determination may be made as to whether a current state of the second object corresponds to at least one of the one or more predicted future states of the first object. In response to a determination that a current state of the second object corresponds to at least one of the predicted future states of the first object, an identifier associated with the second object is updated to correspond to an identifier associated with the first object.

Real-time persistent object tracking for intelligent video analytics systems

In various examples, image areas may be extracted from a batch of one or more images and may be scaled, in batch, to one or more template sizes. Where the image areas include search regions used for localization of objects, the scaled search regions may be loaded into Graphics Processing Unit (GPU) memory and processed in parallel for localization. Similarly, where image areas are used for filter updates, the scaled image areas may be loaded into GPU memory and processed in parallel for filter updates. The image areas may be batched from any number of images and/or from any number of single- and/or multi-object trackers. Further aspects of the disclosure provide approaches for associating locations using correlation response values, for learning correlation filters in object tracking based at least on focused windowing, and for learning correlation filters in object tracking based at least on occlusion maps.

Multi-object tracking using correlation filters in video analytics applications

Apparatuses, systems, and techniques for managing lost objects in an intelligent video analytics system. A first set of application modules is executed for an object tracking application configured to track, based on images depicting an environment, a state of objects included in the environment. The first set of application modules is associated with a first object tracker type. A request is received to configure the object tracking application to execute a second set of application modules associated with a second object tracker type. The second set of application modules includes one or more application modules that are different from application modules of the first set of application modules. The object tracking application is configured to execute the second set of application modules in accordance with the request. The second set of application modules is executed for the object tracking application to track, based on the images depicting the environment, the state of the objects included in the environment.

Dynamically composable object tracker configuration for intelligent video analytics systems

Embodiments of the present disclosure relate to workload-based dynamic throttling of video processing functions. Systems and methods are disclosed that dynamically throttle video processing and/or streaming based on a workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine may perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object trajectories, alerts, object labels, object counts, boundary crossings, intersection highlighting, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on the policies and the workload data, the video streaming and/or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

Growing community of inventors

Santa Clara, CA, United States of America

Joonhwa Shin