Attention-Based Object Tracking Across Frames With Lower Compute
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Solution Overview
Problem
Traditional object tracking systems in autonomous vehicles face challenges with high computational overhead, sensitivity to detection errors, and the need for complex model tuning, making them difficult to deploy on systems lacking powerful hardware.
Innovation Solution
The implementation of sequence-to-sequence tracking methods using state-of-the-art transformer technology, which processes entire sequences of objects rather than relying on incremental entity-by-entity analysis, and avoids surrogate tasks and heuristic model parameter tuning, enabling fast and accurate identification and tracking of objects across multiple frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object tracking systems are implemented in autonomous vehicles, then tracking functionality is provided, but computational overhead becomes high and deployment on systems lacking powerful hardware becomes difficult
Solution Approach 1:
The tracking system is divided into separate modules: a detection model that generates object proposals and a prediction model that performs tracking. This segmentation allows each module to be optimized independently, reducing overall computational overhead while maintaining tracking accuracy.
Solution Approach 2:
The patent introduces an intermediary prediction model that receives detections from the detection model and generates tracks. This intermediary layer decouples the complex detection process from tracking, reducing computational burden on the tracking system itself while improving reliability through specialized tracking algorithms.
2Reliability
If traditional object tracking systems are implemented in autonomous vehicles, then tracking functionality is provided, but sensitivity to detection errors increases
Solution Approach 1:
The prediction model uses feedback from multiple detection results across different frames to refine track predictions. By incorporating historical detection data and using recurrent neural networks, the system can correct detection errors over time and maintain robust tracking even when individual detections are inaccurate.
Solution Approach 2:
The system performs preliminary detection and filtering before final track assignment. By pre-processing detection results and removing obvious false positives before the tracking association step, the system reduces sensitivity to detection errors in the main tracking pipeline.
3Measurement precision
If traditional object tracking systems are implemented in autonomous vehicles, then tracking functionality is provided, but the need for complex model tuning increases
Solution Approach 1:
The prediction model is designed as a universal architecture that can handle multiple tracking scenarios and object types without requiring scenario-specific tuning. The model uses general-purpose attention mechanisms and loss functions that work across different driving conditions, reducing the need for complex model tuning while maintaining high tracking accuracy.
Data Source
AI summary
The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain a plurality of images associated with a corresponding time of a plurality of times. The system further includes a data processing system operatively coupled to the sensing system and configured to generate a plurality of sets of feature tensors (FTs) associated with one or more objects of the environment depicted in a respective image. The data processing system is further to obtain a combined FT and process the combined FT using a neural network to identify one or more tracks characterizing motion of a respective object.