Novel view synthesis methods, such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), rely on Structure-from-Motion (SfM) pipelines like COLMAP for camera parameter estimation. However, these pipelines are prone to errors due to factors like doppelgangers and perspective distortion. While edge devices (e.g., mobile phones) capture images with embedded GPS and IMU data, this meta-data is often noisy. We introduce MetaReg, a robust pipeline that improves camera parameter estimation by leveraging noisy GPS metadata. MetaReg enhances COLMAP with pre-and post-processing steps: the preprocessing stage estimates image overlap using metadata to reduce unnecessary image matching, while the post-processing stage aligns estimated camera coordinates with the world coordinate system using noisy GPS priors. Experiments on challenging datasets demonstrate that MetaReg significantly improves camera parameter estimation, enhancing robustness and accuracy.

In this paper, we propose a novel Non-Overlapping Camera Network Tracking Dataset (CamNeT) for evaluating multi-target tracking algorithms. The dataset is composed of five to eight cameras covering both indoor and outdoor scenes at a university. This dataset consists of six scenarios. Within each scenario are challenges relevant to lighting changes, complex topographies, crowded scenes, and changing grouping dynamics. Persons with predefined trajectories are combined with persons with random trajectories. Ground truth data for predefined trajectories is provided for each camera. Also, a baseline multi-target tracking system is presented. The tracking results using the baseline system are provided, which can be compared with future works. The work provides a comprehensive multicamera dataset for performance evaluation in this challenging application domain, as well as an initial set of results.

Maintaining the stability of tracks on multiple targets in video over extended time periods remains a challenging problem. A few methods which have recently shown encouraging results in this direction rely on learning context models or the availability of training data. However, this may not be feasible in many application scenarios. Moreover, tracking methods should be able to work across multiple resolutions of the video. In this paper, we consider the problem of long-term tracking in video in application domains where context information is not available a priori, nor can it be learned online. We build our solution on the hypothesis that most existing trackers can obtain reasonable short-term tracks (tracklets). By analyzing the statistical properties of these tracklets, we develop associations between them so as to come up with longer tracks. On multiple real-life video sequences spanning low and high resolution data, we show the ability to accurately track over extended time periods.