Tracking a walking person using activity-guided annealed particle filtering

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guidesampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-by-synthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth mo-tion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower ab-solute and relative error scores for both monocular and 2-camera sequences using 80% fewer particles. © 2008 IEEE

Towards a smart smoking cessation app: a 1D-CNN model predicting smoking events

Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app

Human activity tracking from moving camera stereo data

We present a method for tracking human activity using observations from a moving narrow-baseline stereo camera. Range data are computed from the disparity between stereo image pairs. We propose a novel technique for calculating weighting scores from range data given body configuration hypotheses. We use a modified Annealed Particle Filter to recover the optimal tracking candidate from a low dimensional latent space computed from motion capture data and constrained by an activity model. We evaluate the method on synthetic data and on a walking sequence recorded using a moving hand-held stereo camera

Behaviour based particle filtering for human articulated motion tracking

This paper presents an approach to human motion tracking using multiple pre-trained activity models for propagation of particles in Annealed Particle Filtering. Hidden Markov models are trained on dimensionally reduced joint angle data to produce models of activity. Particles are divided between models for propagation by HMM synthesis, before converging on a solution during the annealing process. The approach facilitates multi-view tracking of unknown subjects performing multiple known activities with low particle numbers

Tracking known and unknown human activites

Tracking known and unknown human activite

Tracking object poses in the context of robust body pose estimates

This work focuses on tracking objects being used by humans. These objects are often small, fast moving and heavily occluded by the user. Attempting to recover their 3D position and orientation over time is a challenging research problem. To make progress we appeal to the fact that these objects are often used in a consistent way. The body poses of different people using the same object tend to have similarities, and, when considered relative to those body poses, so do the respective object poses. Our intuition is that, in the context of recent advances in body-pose tracking from RGB-D data, robust object-pose tracking during human-object interactions should also be possible. We propose a combined generative and discriminative tracking framework able to follow gradual changes in object-pose over time but also able to re-initialise object-pose upon recognising distinctive body-poses. The framework is able to predict object-pose relative to a set of independent coordinate systems, each one centred upon a different part of the body. We conduct a quantitative investigation into which body parts serve as the best predictors of object-pose over the course of different interactions. We find that while object-translation should be predicted from nearby body parts, object-rotation can be more robustly predicted by using a much wider range of body parts. Our main contribution is to provide the first object-tracking system able to estimate 3D translation and orientation from RGB-D observations of human-object interactions. By tracking precise changes in object-pose, our method opens up the possibility of more detailed computational reasoning about human-object interactions and their outcomes. For example, in assistive living systems that go beyond just recognising the actions and objects involved in everyday tasks such as sweeping or drinking, to reasoning that a person has missed sweeping under the chair or not drunk enough water today. © 2014 Elsevier B.V. All rights reserved

Object localisation via action recognition

The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified between action classes by a Support Vector Machine and converted to discriminative object pose hypotheses using a {body, object} pose mapping. They are then mixed with generative hypotheses by the importance sampler and evaluated against the image. The approach out-performs a state of the art adaptive tracker for localisation of 14/15 test implements and additionally gives object classifications and 3D object pose estimates

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention

Backing off: hierarchical decomposition of activity for 3D novel pose recovery

For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, low-dimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to be recovered, but is both difficult and expensive. This paper explores a potential middle ground between these approaches, using the hierarchical Gaussian process latent variable model to learn activity at different hierarchical scales within the human skeleton. We show that by training on full-body activity data then descending through the hierarchy in stages and exploring subtrees independently of one another, novel poses may be recovered. Experimental results on motion capture data and monocular video sequences demonstrate the utility of the approach, and comparisons are drawn with existing low-dimensional activity models. © 2009. The copyright of this document resides with its authors

Backing off: hierarchical decomposition of activity for 3D novel pose recovery

For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, low-dimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to be recovered, but is both difficult and expensive. This paper explores a potential middle ground between these approaches, using the hierarchical Gaussian process latent variable model to learn activity at different hierarchical scales within the human skeleton. We show that by training on full-body activity data then descending through the hierarchy in stages and exploring subtrees independently of one another, novel poses may be recovered. Experimental results on motion capture data and monocular video sequences demonstrate the utility of the approach, and comparisons are drawn with existing low-dimensional activity models. © 2009. The copyright of this document resides with its authors