Egocentric vision-based passive dietary intake monitoring

Egocentric (first-person) perception captures and reveals how people perceive their surroundings. This unique perceptual view enables passive and objective monitoring of human-centric activities and behaviours. In capturing egocentric visual data, wearable cameras are used. Recent advances in wearable technologies have enabled wearable cameras to be lightweight, accurate, and with long battery life, making long-term passive monitoring a promising solution for healthcare and human behaviour understanding. In addition, recent progress in deep learning has provided an opportunity to accelerate the development of passive methods to enable pervasive and accurate monitoring, as well as comprehensive modelling of human-centric behaviours. This thesis investigates and proposes innovative egocentric technologies for passive dietary intake monitoring and human behaviour analysis. Compared to conventional dietary assessment methods in nutritional epidemiology, such as 24-hour dietary recall (24HR) and food frequency questionnaires (FFQs), which heavily rely on subjects’ memory to recall the dietary intake, and trained dietitians to collect, interpret, and analyse the dietary data, passive dietary intake monitoring can ease such burden and provide more accurate and objective assessment of dietary intake. Egocentric vision-based passive monitoring uses wearable cameras to continuously record human-centric activities with a close-up view. This passive way of monitoring does not require active participation from the subject, and records rich spatiotemporal details for fine-grained analysis. Based on egocentric vision and passive dietary intake monitoring, this thesis proposes: 1) a novel network structure called PAR-Net to achieve accurate food recognition by mining discriminative food regions. PAR-Net has been evaluated with food intake images captured by wearable cameras as well as those non-egocentric food images to validate its effectiveness for food recognition; 2) a deep learning-based solution for recognising consumed food items as well as counting the number of bites taken by the subjects from egocentric videos in an end-to-end manner; 3) in light of privacy concerns in egocentric data, this thesis also proposes a privacy-preserved solution for passive dietary intake monitoring, which uses image captioning techniques to summarise the image content and subsequently combines image captioning with 3D container reconstruction to report the actual food volume consumed. Furthermore, a novel framework that integrates food recognition, hand tracking and face recognition has also been developed to tackle the challenge of assessing individual dietary intake in food sharing scenarios with the use of a panoramic camera. Extensive experiments have been conducted. Tested with both laboratory (captured in London) and field study data (captured in Africa), the above proposed solutions have proven the feasibility and accuracy of using the egocentric camera technologies with deep learning methods for individual dietary assessment and human behaviour analysis.Open Acces

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

3D GLOH features for human action recognition

Human action recognition from videos has wide applicability and receives significant interests. In this work, to better identify spatio-temporal characteristics, we propose a novel 3D extension of Gradient Location and Orientation Histograms, which provides discriminative local features representing not only the gradient orientation, but also their relative locations. We further propose a human action recognition system based on the Bag of Visual Words model, by combining the new 3D GLOH local features with Histograms of Oriented Optical Flow (HOOF) global features. Along with the idea from our recent work to extract features only in salient regions, our overall system outperforms existing feature descriptors for human action recognition for challenging real-world video datasets

Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition

This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of midlevel features, we develop a spectral embedding approach to latent semantic learning based on L1-graph, without the need to tune any parameter for graph construction as a key step of manifold learning. More importantly, we construct the L1-graph with structured sparse representation, which can be obtained by structured sparse coding with its structured sparsity ensured by novel L1-norm hypergraph regularization over mid-level features. In the new embedding space, we learn latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for human action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both L1-graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the superior performance of our method.Comment: The short version of this paper appears in ICCV 201