Image and video content analysis applications typically require functionalities such as object classification, detection and tracking, and activity recognition. Objects may undergo translation, rotation, and changes in scale due to perspective projection. Further, the appearance of objects and illumination conditions may change over time. Occasionally objects might also occlude one another in the scene making consistent classification, detection, and tracking a challenge. To reduce the effect of these limitations it is proposed to model objects in wavelets domain using silhouettes. The silhouette of an object is characterized using projected histograms of sixteen wavelet primitives extracted from a silhouette map of the scene. A classifier based on eigen decomposition of histogram of feature vectors combined with sparse coding prediction is presented. The model of a class is represented as over complete dictionary of sparse codes. For robustness multiple classifiers based on the same sparse code operate in parallel but at different scales. It is combined with spatial histogram classifier to realize a bank of multiple classifiers. The accuracy of the proposed classifier is compared with support vector machine and published state-of the-art results. Accuracy evaluation and real-time performance demonstrates competitive performance with the published stat-of-the-art results.

Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.

Background: Healthcare professionals render various healthcare services to patients. However, in their duty to patients, they are exposed to occupational hazards that could be detrimental to their health and safety. In order to minimize exposure to these occupational hazards and prevent their detrimental effects on healthcare professionals, it is fundamental to assess the level of knowledge healthcare professionals have with regards to these health hazards. Objectives: The study was intended at assessing the knowledge of healthcare professionals at New Abirem Government Hospital on occupational health hazards and safety practices at the hospital. Methodology: A cross-sectional quantitative study approach was adopted in this study. A total of 171 participants were recruited from within the staff at the New Abirem Hospital. Simple random sampling technique was used to select the total 171 from the staff of 300. Questionnaires were administered to obtain data for the study and the administration body was interviewed. For data analysis, the quantitative data was edited and cleaned using Statistical Package for Social Sciences (SPSS) version 20. Basic descriptive analysis was thereafter performed. In the analysis and interpretation of the quantitative data, the statistical Mean was used. Results and Findings: The results showed that 120 (70.2%) participants agreed that, knowledge of occupational health and safety is the responsibility and right of both employer and employee. 33 (19.3%) of the participants were neutral, 18 (10.5%) disagreed with the assertion. Furthermore, 129 (75.4%) of the participants agreed that occupational hazards always relate to work activities that increase the risk of injury. 23 (13.5%) of the respondents were neutral in their response whiles 19 (11.1%) disagreed with the assertion. Additionally, 117 (75.4%) of the participants stated they were obliged to report work-related accidents or injuries even though 24 (14%) disagreed. Nevertheless, 114 (66.7%) agreed that the most effective accident and disease prevention begins when work processes are still in the design stage. Similarly, 126 (73.7%) respondents agreed that healthcare professionals are at high risk of occupational hazard. The study found out that there were no knowledge of laid down health and safety policies in place at the hospital. This was quite unexpected as the healthcare facility is considered a high-risk facility. There were no in-house health and safety personnel. The hospital depended heavily on periodic trainings offered to staff to keep them up-to-date on health and safety issues. The study also revealed major challenges such as lack of funding, understaffing, bureaucracy and non-compliance to internal rules and regulations as barriers to ensuring effective occupational health and safety. Conclusion: Healthcare professionals are well knowledgeable of occupational hazards at the facility. The highest form of occupational hazard that the healthcare professionals are exposed to is chemical hazards. There are several problems militating against the top management in improving upon the occupational safety at the workplace. However, more could be done to ensure a more secured work environment for employees of the hospital.

The chapter reviews recent developments in cognitive robotics, challenges and opportunities brought by new developments in machine learning (ML) and information communication technology (ICT), with a view to simulating research. To draw insights into the current trends and challenges, a review of algorithms and systems is undertaken. Furthermore, a case study involving human activity recognition, as well as face and emotion recognition, is also presented. Open research questions and future trends are then presented.