End-to-End Differentiable Multi-View Tracking: Architecture and Fine-Tuning Experiments

In this work, we develop an end-to-end differentiable multi-view visual tracking architecture and explore fine-tuning model parameters via gradient-based optimization and automatic differentiation. We consider a setting with multiple camera nodes distributed in the tracking environment that collaboratively track objects. The architecture that we construct includes within-image-plane deep learning-based detection models, probabilistic camera models, object dynamics models, and an $N$-object Kalman filter-based tracking model. We demonstrate fully differentiable choices for each of these components, enabling learning and fine-tuning of the parameters of all system components based on different forms of supervision. Our results show performance gains for $N$-object tracking when fine-tuning the parameters of the system for end-to-end tracking performance.