# Generic Multiview Visual Tracking

## TL;DR

This paper introduces a versatile multiview visual tracking framework that handles camera movement and occlusion without requiring specific object models or calibration, utilizing a novel trajectory prediction network and collaborative filters.

## Contribution

It presents a generic multiview tracking system with a cross-camera trajectory prediction network and collaborative correlation filter, capable of operating with moving cameras and without prior calibration.

## Key findings

- Outperforms state-of-the-art methods on GMTD and PETS2009 datasets.
- Introduces a new multiview tracking dataset (GMTD).
- Demonstrates robustness to occlusion and view change.

## Abstract

Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets (e.g. human), static cameras, and/or camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation in our framework is a cross-camera trajectory prediction network (TPN), which implicitly and dynamically encodes camera geometric relations, and hence addresses missing target issues such as occlusion. Moreover, during tracking, we assemble information across different cameras to dynamically update a novel collaborative correlation filter (CCF), which is shared among cameras to achieve robustness against view change. The two components are integrated into a correlation filter tracking framework, where the features are trained offline using existing single view tracking datasets. For evaluation, we first contribute a new generic multiview tracking dataset (GMTD) with careful annotations, and then run experiments on GMTD and the PETS2009 datasets. On both datasets, the proposed GMT algorithm shows clear advantages over state-of-the-art ones.