7 research outputs found
Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters
With the rise of end-to-end learning through deep learning, person detectors
and re-identification (ReID) models have recently become very strong.
Multi-camera multi-target (MCMT) tracking has not fully gone through this
transformation yet. We intend to take another step in this direction by
presenting a theoretically principled way of integrating ReID with tracking
formulated as an optimal Bayes filter. This conveniently side-steps the need
for data-association and opens up a direct path from full images to the core of
the tracker. While the results are still sub-par, we believe that this new,
tight integration opens many interesting research opportunities and leads the
way towards full end-to-end tracking from raw pixels.Comment: First two authors have equal contribution. This is initial work into
a new direction, not a benchmark-beating method. v2 only adds
acknowledgements and fixes a typo in e-mai
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.Comment: doctoral dissertatio
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues in a coherent end-to-end fashion over a long period of time. However, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects. To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. We are able to correct many data association errors and recover observations from an occluded state. We demonstrate the robustness of our data-driven approach by tracking multiple targets using their appearance, motion, and even interactions. Our method outperforms previous works on multiple publicly available datasets including the challenging MOT benchmark
Introduction: Ways of Machine Seeing
How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives.
This 'editorial' is for a special issue of AI & Society, which includes contributions from: MarĂa JesĂşs Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chávez Heras, Vladan Joler, Nicolas MalevĂ©, Lev Manovich, Nicholas Mirzoeff, Perle Møhl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen
Tracking social groups within and across cameras
We propose a method for tracking groups from single and multiple cameras with disjoint fields of view. Our formulation follows the tracking-by-detection paradigm where groups are the atomic entities and are linked over time to form long and consistent trajectories. To this end, we formulate the problem as a supervised clustering problem where a Structural SVM classifier learns a similarity measure appropriate for group entities. Multi-camera group tracking is handled inside the framework by adopting an orthogonal feature encoding that allows the classifier to learn inter- and intra-camera feature weights differently. Experiments were carried out on a novel annotated group tracking data set, the DukeMTMC-Groups data set. Since this is the first data set on the problem it comes with the proposal of a suitable evaluation measure. Results of adopting learning for the task are encouraging, scoring a +15% improvement in F1 measure over a non-learning based clustering baseline. To our knowledge this is the first proposal of this kind dealing with multi-camera group tracking