33 research outputs found
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects.Comment: ICRA'18 submissio
Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate
3D panoramic multi-person localization and tracking are prominent in many
applications, however, conventional methods using LiDAR equipment could be
economically expensive and also computationally inefficient due to the
processing of point cloud data. In this work, we propose an effective and
efficient approach at a low cost. First, we obtain panoramic videos with four
normal cameras. Then, we transform human locations from a 2D panoramic image
coordinate to a 3D panoramic camera coordinate using camera geometry and human
bio-metric property (i.e., height). Finally, we generate 3D tracklets by
associating human appearance and 3D trajectory. We verify the effectiveness of
our method on three datasets including a new one built by us, in terms of 3D
single-view multi-person localization, 3D single-view multi-person tracking,
and 3D panoramic multi-person localization and tracking. Our code and dataset
are available at \url{https://github.com/fandulu/MPLT}.Comment: 5 page