The development of a video retrieval system using a clinician-led approach

Patient video taken at home can provide valuable insights into the recovery progress during a programme of physical therapy, but is very time consuming for clinician review. Our work focussed on (i) enabling any patient to share information about progress at home, simply by sharing video and (ii) building intelligent systems to support Physical Therapists (PTs) in reviewing this video data and extracting the necessary detail. This paper reports the development of the system, appropriate for future clinical use without reliance on a technical team, and the clinician involvement in that development. We contribute an interactive content-based video retrieval system that significantly reduces the time taken for clinicians to review videos, using human head movement as an example. The system supports query-by-movement (clinicians move their own body to define search queries) and retrieves the essential fine-grained movements needed for clinical interpretation. This is done by comparing sequences of image-based pose estimates (here head rotations) through a distance metric (here Fréchet distance) and presenting a ranked list of similar movements to clinicians for review. In contrast to existing intelligent systems for retrospective review of human movement, the system supports a flexible analysis where clinicians can look for any movement that interests them. Evaluation by a group of PTs with expertise in training movement control showed that 96% of all relevant movements were identified with time savings of as much as 99.1% compared to reviewing target videos in full. The novelty of this contribution includes retrospective progress monitoring that preserves context through video, and content-based video retrieval that supports both fine-grained human actions and query-by-movement. Future research, including large clinician-led studies, will refine the technical aspects and explore the benefits in terms of patient outcomes, PT time, and financial savings over the course of a programme of therapy. It is anticipated that this clinician-led approach will mitigate the reported slow clinical uptake of technology with resulting patient benefit

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Deep Head Pose Estimation from Depth Data for In-car Automotive Applications

Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, extit{Biwi Kinect Head Pose}, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements

Distance Estimation of an Unknown Person from a Portrait

We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each photographed from seven distances. We find that our proposed method outperforms humans performing the same task. We observe that different physiognomies will bias systematically the estimate of distance, i.e. some people look closer than others. We expire which landmarks are more important for this task