Multiple-Object Tracking With Transformer-Based Motion Matching
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Solution Overview
Problem
Existing object tracking systems struggle to accurately track multiple objects with varying motions and similar appearances, particularly in scenarios with limited overlap between frames.
Innovation Solution
A neural network model incorporating a transformer, ReID embedding module, motion estimation module, and matching module is used to detect objects, match them across frames, and estimate their trajectories, utilizing features from both appearance and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking methods are used, then the system is simpler to implement, but the tracking accuracy deteriorates for objects with similar appearances and varying motions
Solution Approach 1:
The tracking system is segmented into multiple specialized modules: a transformer-based feature extraction module that processes spatial and temporal features separately, a ReID embedding module that extracts appearance features, and a motion estimation module that handles motion patterns. Each module focuses on specific aspects of tracking, improving overall accuracy while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The system uses composite feature representations by fusing appearance features from ReID embedding with motion features from motion estimation. This composite approach combines multiple types of information (spatial, temporal, appearance, motion) to create a robust tracking system that handles both similar appearances and varying motions effectively.
2Reliability
If appearance-only features are used for tracking, then the computational complexity is lower, but the tracking reliability deteriorates in scenarios with limited frame overlap
Solution Approach 1:
The system merges appearance features and motion features into a unified tracking framework. The transformer architecture integrates spatial features from ReID embeddings with temporal features from motion estimation, creating a combined representation that improves reliability in scenarios with limited frame overlap by leveraging both appearance consistency and motion continuity.
Solution Approach 2:
The transformer-based attention mechanism serves as an intermediary that selectively weights and integrates appearance and motion features. This attention mechanism mediates between the two feature types, allowing the system to dynamically adjust the contribution of each feature type based on the specific tracking scenario, thereby improving reliability without simply combining all features equally.
3Adaptability or versatility
If a comprehensive neural network model with multiple modules is used, then the adaptability to different tracking scenarios improves, but the computational cost increases
Solution Approach 1:
The system employs dynamic feature processing where the transformer attention mechanism adaptively adjusts the weighting of spatial and temporal features based on the specific tracking scenario. This dynamic approach allows the model to focus computational resources on the most relevant features for each situation, improving scenario adaptability while managing computational energy consumption by avoiding uniform high-cost processing of all features in all cases.
Data Source
AI summary
An operating method of an electronic device, according to an example embodiment, may include detecting at least one object in a frame at a current time point. The operating method may include obtaining, using a neural network model, a matching result between the at least one detected object and at least one object tracked in frames at a previous time point that precedes the current time point, based on information about the at least one detected object, information about the at least one tracked object, a frame feature of the frame, and an object-recognized frame feature of the previous time point. The operating method may include obtaining trajectory information of the at least one detected object up to the current time point, based on the matching result.