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STMMOT: Advancing multi-object tracking through spatiotemporal memory networks and multi-scale attention pyramids.
Hamza Mukhtar1, Muhammad Usman Ghani Khan1
1Department of Computer Science, University of Engineering and Technology Lahore, G.T. Road, Lahore, 54890, Punjab, Pakistan; Intelligent Criminology Lab, National Center of Artificial Intelligence, AlKhawarizmi Institute of Computer Science, University of Engineering and Technology, GT, Road, Lahore, 54890, Punjab, Pakistan.
This study introduces STMMOT, a new method for multi-object tracking that improves performance in challenging scenarios like occlusion. STMMOT enhances object detection and identity linkage, outperforming previous methods.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Robotics
Background:
- Multi-object Tracking (MOT) is crucial for applications like surveillance and autonomous driving.
- Existing MOT methods struggle with non-uniform movements, occlusion, and appearance-reappearance challenges.
Purpose of the Study:
- To develop a comprehensive MOT method that integrates object detection and identity linkage.
- To enhance long-term object tracking capabilities, especially in complex scenarios.
Main Methods:
- Introduced STMMOT, an end-to-end trainable framework with four key modules: candidate proposal network, scale variant pyramid, spatio-temporal memory encoder, and decoder.
- Utilized a vision-Transformer encoder-decoder for proposal generation and a dynamic query embedding approach for continuous object state updates.
- Employed an attention-based aggregator within the spatio-temporal memory module to encode historical object states.
Main Results:
- STMMOT achieved high scores on MOT17 and MOT20 datasets, including IDF1 (79.8, 78.4), MOTA (79.3, 74.1), and HOTA (73.2, 69.0).
- Demonstrated significant improvements over TransMOT, with a 4.58% increase in IDF1 and reduced ID switching by up to 21.05%.
- Maintained low ID switch counts (1529 on MOT17, 1264 on MOT20).
Conclusions:
- STMMOT offers a robust and effective solution for multi-object tracking, particularly in scenarios with occlusions and non-uniform motion.
- The proposed architecture successfully merges detection and association, eliminating the need for post-processing.
- The dynamic query embedding and memory module contribute to superior long-term tracking performance.
