20240013409. Systems and Methods for Multi-Object Tracking simplified abstract (Toyota Research Institute, Inc.)

Systems and Methods for Multi-Object Tracking

Organization Name

Toyota Research Institute, Inc.

Inventor(s)

Colton Stearns of Stanford CA (US)

Jie Li of Los Altos CA (US)

Rares A. Ambrus of San Francisco CA (US)

Vitor Campagnolo Guizilini of Santa Clara CA (US)

Sergey Zakharov of San Francisco CA (US)

Adrien D. Gaidon of San Jose CA (US)

Davis Rempe of Stanford CA (US)

Tolga Birdal (US)

Leonidas J. Guibas of Palo Alto CA (US)

Systems and Methods for Multi-Object Tracking - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013409 titled 'Systems and Methods for Multi-Object Tracking

Simplified Explanation

The patent application describes a method for multiple object tracking using a point cloud dataset. Here is a simplified explanation of the abstract:

• The method involves receiving a point cloud dataset and detecting objects within it using a computing device.
• Each detected object is defined by points in the point cloud dataset and a bounding box.
• Historical tracklets, which represent the past states of the detected objects, are queried.
• The method implements a 4D encoding backbone with two branches.
• The first branch computes per-point features for the detected objects and their corresponding historical tracklet states.
• The second branch obtains 4D point features.
• The per-point features and 4D point features are concatenated.
• A decoder predicts the current tracklet states for each detected object using the concatenated features.

Potential applications of this technology:

• Autonomous vehicles: This method can be used for object tracking in self-driving cars, helping them navigate and avoid collisions.
• Surveillance systems: The technology can be applied to track multiple objects in real-time, enhancing security and monitoring capabilities.
• Robotics: Object tracking is essential in robotics applications, such as warehouse automation or industrial robots, to ensure accurate and efficient operations.

Problems solved by this technology:

• Accurate object tracking: The method improves the accuracy of object tracking by utilizing historical tracklet states and per-point features.
• Handling multiple objects: The technology addresses the challenge of tracking multiple objects simultaneously in a point cloud dataset.
• Real-time tracking: The method enables real-time tracking of objects, which is crucial for applications like autonomous vehicles or surveillance systems.

Benefits of this technology:

• Enhanced safety: Accurate and real-time object tracking can contribute to safer environments, especially in autonomous vehicles and surveillance systems.
• Improved efficiency: The method allows for efficient tracking of multiple objects, optimizing processes in various industries like logistics or manufacturing.
• Advanced analytics: The technology provides valuable data on object movements and behaviors, enabling better decision-making and analysis.

Original Abstract Submitted

a method for multiple object tracking includes receiving, with a computing device, a point cloud dataset, detecting one or more objects in the point cloud dataset, each of the detected one or more objects defined by points of the point cloud dataset and a bounding box, querying one or more historical tracklets for historical tracklet states corresponding to each of the one or more detected objects, implementing a 4d encoding backbone comprising two branches: a first branch configured to compute per-point features for each of the one or more objects and the corresponding historical tracklet states, and a second branch configured to obtain 4d point features, concatenating the per-point features and the 4d point features, and predicting, with a decoder receiving the concatenated per-point features, current tracklet states for each of the one or more objects.