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Found 6 papers, 1 papers with code
Towards Deep Clustering of Human Activities from Wearables
Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data.
Joint Probabilistic Matching Using m-Best Solutions
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.
Efficient Point Process Inference for Large-Scale Object Detection
We tackle the problem of large-scale object detection in images, where the number of objects can be arbitrarily large, and can exhibit significant overlap/occlusion.
Online Multi-Target Tracking Using Recurrent Neural Networks
Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking.
Joint Probabilistic Data Association Revisited
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
To deal with this, we propose a bootstrap filter composed of an estimator and a tracker.
