Temporal Sensor Object Tracking Using Motion Distortion Cues
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Solution Overview
Problem
Temporal sensors often experience latency-related issues, leading to distorted representations of dynamic objects, which can affect the accuracy of object attributes and tracking in various applications.
Innovation Solution
The techniques described utilize distorted representations of objects in temporal sensor data to infer object attributes such as velocities, bounding boxes, and orientations, by comparing data from multiple sensors with different scanning directions or using machine-learned models to process time-dimensional sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If temporal sensors are used to capture data over a period of time, then more complete information about dynamic scenes is obtained, but latency-related issues cause distorted representations of objects
Solution Approach 1:
The patent converts the harmful distortion caused by temporal sensor latency into a beneficial signal. By analyzing the distortion pattern in temporal sensor data, the system infers object velocity information that would otherwise be lost. The distortion, caused by the time delay between scanning different portions of the scene, actually encodes motion information that can be extracted and used for accurate object tracking and velocity estimation.
2Area of stationary object
If temporal sensors scan dynamic scenes, then coverage of the scene is improved, but object representations become distorted relative to actual shape
Solution Approach 1:
The patent transforms the shape distortion artifact into useful velocity information. The distortion occurs because different portions of the scene are scanned at different times, and objects move during this scanning period. By measuring the magnitude and direction of this distortion, the system recovers accurate object velocity vectors, converting a detrimental effect into a beneficial measurement capability.
Solution Approach 2:
The system changes the interpretation parameter of sensor data from treating distortion as an error to be corrected to treating distortion as a signal to be measured. By analyzing the temporal dimension of the distortion and relating it to the scanning geometry, the system extracts velocity parameters that compensate for the shape inaccuracies and enable accurate object tracking.
Data Source
AI summary
Techniques for determining attributes associated with objects represented in temporal sensor data. In some examples, the techniques may include receiving sensor data including a representation of an object in an environment. The sensor data may be generated by a temporal sensor of a vehicle and, in some instances, a trajectory of the object or the vehicle may contribute to a distortion in the representation of the object. For instance, a shape of the representation of the object may be distorted relative to an actual shape of the object. The techniques may also include determining an attribute (e.g., velocity, bounding box, etc.) associated with the object based at least in part on a difference between the representation of the object and another representation of the object (e.g., in other sensor data) or the actual shape of the object.