4,026 research outputs found
Recognition, Analysis, and Assessments of Human Skills using Wearable Sensors
One of the biggest social issues in mature societies such as Europe and Japan
is the aging population and declining birth rate. These societies have a serious
problem with the retirement of the expert workers, doctors, and engineers etc.
Especially in the sectors that require long time to make experts in fields like medicine and industry; the retirement and injuries of the experts, is a serious problem. The technology to support the training and assessment of skilled workers (like doctors, manufacturing
workers) is strongly required for the society. Although there are some solutions for
this problem, most of them are video-based which violates the privacy of the subjects.
Furthermore, they are not easy to deploy due to the need for large training data.
This thesis provides a novel framework to recognize, analyze, and assess human
skills with minimum customization cost. The presented framework tackles this problem
in two different domains, industrial setup and medical operations of catheter-based
cardiovascular interventions (CBCVI).
In particular, the contributions of this thesis are four-fold. First, it proposes an
easy-to-deploy framework for human activity recognition based on zero-shot learning
approach, which is based on learning basic actions and objects. The model recognizes
unseen activities by combinations of basic actions learned in a preliminary way and involved objects. Therefore, it is completely configurable by the user and can be used to detect completely new activities.
Second, a novel gaze-estimation model for attention driven object detection task is
presented. The key features of the model are: (i) usage of the deformable convolutional
layers to better incorporate spatial dependencies of different shapes of objects and
backgrounds, (ii) formulation of the gaze-estimation problem in two different way, as a
classification as well as a regression problem. We combine both formulations using a
joint loss that incorporates both the cross-entropy as well as the mean-squared error in
order to train our model. This enhanced the accuracy of the model from 6.8 by using only
the cross-entropy loss to 6.4 for the joint loss.
The third contribution of this thesis targets the area of quantification of quality of
i
actions using wearable sensor. To address the variety of scenarios, we have targeted two
possibilities: a) both expert and novice data is available , b) only expert data is available,
a quite common case in safety critical scenarios.
Both of the developed methods from these scenarios are deep learning based. In the
first one, we use autoencoders with OneClass SVM, and in the second one we use the
Siamese Networks. These methods allow us to encode the expert’s expertise and to learn
the differences between novice and expert workers. This enables quantification of the
performance of the novice in comparison to the expert worker.
The fourth contribution, explicitly targets medical practitioners and provides a
methodology for novel gaze-based temporal spatial analysis of CBCVI data. The developed
methodology allows continuous registration and analysis of gaze data for analysis
of the visual X-ray image processing (XRIP) strategies of expert operators in live-cases scenarios and may assist in transferring experts’ reading skills to novices
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges
Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed
Applying psychological science to the CCTV review process: a review of cognitive and ergonomic literature
As CCTV cameras are used more and more often to increase security in communities, police are spending a larger proportion of their resources, including time, in processing CCTV images when investigating crimes that have occurred (Levesley & Martin, 2005; Nichols, 2001). As with all tasks, there are ways to approach this task that will facilitate performance and other approaches that will degrade performance, either by increasing errors or by unnecessarily prolonging the process. A clearer understanding of psychological factors influencing the effectiveness of footage review will facilitate future training in best practice with respect to the review of CCTV footage. The goal of this report is to provide such understanding by reviewing research on footage review, research on related tasks that require similar skills, and experimental laboratory research about the cognitive skills underpinning the task. The report is organised to address five challenges to effectiveness of CCTV review: the effects of the degraded nature of CCTV footage, distractions and interrupts, the length of the task, inappropriate mindset, and variability in people’s abilities and experience. Recommendations for optimising CCTV footage review include (1) doing a cognitive task analysis to increase understanding of the ways in which performance might be limited, (2) exploiting technology advances to maximise the perceptual quality of the footage (3) training people to improve the flexibility of their mindset as they perceive and interpret the images seen, (4) monitoring performance either on an ongoing basis, by using psychophysiological measures of alertness, or periodically, by testing screeners’ ability to find evidence in footage developed for such testing, and (5) evaluating the relevance of possible selection tests to screen effective from ineffective screener
Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem
Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods
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Detection of abandoned objects in crowded environments
With concerns about terrorism and global security on the rise, it has become vital to have in place efficient threat detection systems that will identify potentially dangerous situations, and alert the authorities to take appropriate action. Of particular relevance is the case of abandoned objects in highly crowded areas. This thesis describes a general framework that recognizes the event of someone leaving an object unattended in forbidden areas. Our approach involves the recognition of four sub-events that characterize the activity of interest. When an unaccompanied object is found, the system analyzes its history to determine its most likely owner(s). Through subsequent frames, the system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the object and determines the appropriate course of action. The system was successfully implemented and tested on several standardized datasets.Electrical and Computer Engineerin
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