Online Multi-Object Tracking With FSM State Transitions
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Solution Overview
Problem
Existing online multi-object tracking systems struggle to achieve real-time performance while maintaining high accuracy, especially in autonomous driving scenarios, as they either lack sufficient speed or performance.
Innovation Solution
A system utilizing finite state machines to model objects, combined with motion and appearance features, and a Kalman filter to reduce noise, divided into stages of similarity calculation, data association, state transition, and post-processing for improved tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning methods (CNN, LSTM) are used for online MOT, then tracking performance is improved, but real-time speed cannot be achieved
Solution Approach 1:
The tracking process is segmented into four distinct stages: similarity calculation, data association, state transition, and post processing. This segmentation allows each stage to be optimized independently, with lightweight models used for real-time processing while maintaining overall tracking performance through coordinated stage execution.
Solution Approach 2:
The system changes the computational parameters by replacing heavy deep learning models with lighter alternatives suitable for real-time processing. Specifically, it uses appearance features (color histograms, HOG) and motion features (Kalman filter predictions) instead of complex CNNs, achieving real-time speed while maintaining acceptable tracking performance.
2Productivity
If lightweight methods (Kalman filter, MDP) are used for real-time tracking, then speed is improved, but tracking performance is insufficient
Solution Approach 1:
The system merges multiple lightweight methods into a unified framework that achieves both real-time speed and improved performance. It combines Kalman filters for motion prediction, appearance features for identification, and a four-stage processing pipeline for comprehensive tracking, surpassing individual lightweight methods while maintaining real-time capability.
Solution Approach 2:
The tracking system uses a composite approach by integrating multiple feature types (appearance features including color histograms and HOG, motion features from Kalman filters) and multiple processing stages. This composite structure achieves performance comparable to heavy deep learning models while maintaining real-time processing speed.
3Reliability
If offline tracking is used, then tracking performance is improved, but online real-time capability is lost
Solution Approach 1:
The system transitions from static offline processing to dynamic online processing by implementing a real-time capable four-stage pipeline. Each stage processes data as it becomes available, with state transitions updating object states continuously based on current frame detections, enabling adaptation to changing scenes in real-time while maintaining performance through coordinated stage execution.
Data Source
AI summary
A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.