Challenge
In the cutting-edge realm of computer vision, Agot AI stands at the forefront with its model-based multi-object tracking system. The quest for perfection led Agot AI to seek an enhancement of their system's core metrics, including IDF1 and MOTA, to ensure unparalleled tracking accuracy and reliability in real-world applications.
Solution
The Rapidev AI team stepped into this high-stakes arena with a dual approach aimed at refining the system's performance. With a deep dive into the latest multi-object tracking literature and armed with advanced PyTorch skills, we innovated on two fronts:
- Model Optimization: Revisiting the custom network architecture, the team introduced strategic modifications to enhance its predictive accuracy and efficiency, ensuring the system's adeptness at tracking multiple objects with precision.
- Post-Processing Excellence: Recognizing the critical role of post-processing in tracking performance, the team implemented sophisticated algorithms to refine the system's output, further elevating the tracking metrics.
Tech. Proficiency
Team’s work exemplified the power of blending cutting-edge research with practical engineering solutions. Their expertise in computer vision fundamentals, coupled with proficiency in software engineering, enabled Agot AI to push the boundaries of what's possible in multi-object tracking.