282 research outputs found
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking
Multi-person human pose estimation and tracking in the wild is important and
challenging. For training a powerful model, large-scale training data are
crucial. While there are several datasets for human pose estimation, the best
practice for training on multi-dataset has not been investigated. In this
paper, we present a simple network called Multi-Domain Pose Network (MDPN) to
address this problem. By treating the task as multi-domain learning, our
methods can learn a better representation for pose prediction. Together with
prediction heads fine-tuning and multi-branch combination, it shows significant
improvement over baselines and achieves the best performance on PoseTrack ECCV
2018 Challenge without additional datasets other than MPII and COCO.Comment: Extended abstract for the ECCV 2018 PoseTrack Worksho
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985
Active Collaborative Ensemble Tracking
A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classification task from different views. However, by updating all of the
ensemble using a shared set of samples and their final labels, such diversity
is lost or reduced to the diversity provided by the underlying features or
internal classifiers' dynamics. Additionally, the classifiers do not exchange
information with each other while striving to serve the collective goal, i.e.,
better classification. In this study, we propose an active collaborative
information exchange scheme for ensemble tracking. This, not only orchestrates
different classifier towards a common goal but also provides an intelligent
update mechanism to keep the diversity of classifiers and to mitigate the
shortcomings of one with the others. The data exchange is optimized with regard
to an ensemble uncertainty utility function, and the ensemble is updated via
co-training. The evaluations demonstrate promising results realized by the
proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio
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