Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, , course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified. In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatial and temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance.
Project Report from the year 2017 in the subject Computer Science - Miscellaneous, grade: BSc Honours in Computer Science, , course: Honors research project, language: English, abstract: Most universities offer a wide range of courses in which students can enrol. As a result, students may feel overwhelmed with the many possibilities and large amount of information, resulting in having a difficult time deciding what to sign up for. To this end, there is a need for a system that can assist students in this crucial process. Thus, we set out to develop a web-based recommender application that could generate a list of valuable, accurate course recommendations, taking into account a student’s likelihood of succeeding academically. Choosing an effective and stimulating set of courses is not an easy task for a student. There are several factors at play when it comes to choosing courses that one must study. One of these factors may be the assumed difficulty of a course that a student is considering to take. Of course, if the course is compulsory, then the student has no choice but to enrol in it. However, in the situation in where there are many different subjects to choose from, the student may shy away from taking optional courses that might pose a significant challenge, in terms of workload or being unable to fully understand the course content. These courses would clearly have a negative effect on academic performance. However, there may also be some students who are looking to be challenged, and for whom choosing more difficult courses would be an exciting challenge. In terms of academic performance, effective course selection is of utmost importance in ensuring a student is able to succeed in her or his studies and obtain their qualification(s).
Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Internet, New Technologies, , course: Machine Learning, language: English, abstract: Human Action Recognition is the task of recognizing a set of actions being performed in a video sequence. Reliably and efficiently detecting and identifying actions in video could have vast impacts in the surveillance, security, healthcare and entertainment spaces. The problem addressed in this paper is to explore different engineered spatial and temporal image and video features (and combinations thereof) for the purposes of Human Action Recognition, as well as explore different Deep Learning architectures for non-engineered features (and classification) that may be used in tandem with the handcrafted features. Further, comparisons between the different combinations of features will be made and the best, most discriminative feature set will be identified. In the paper, the development and implementation of a robust framework for Human Action Recognition was proposed. The motivation behind the proposed research is, firstly, the high effectiveness of gradient-based features as descriptors - such as HOG, HOF, and N-Jets - for video-based human action recognition. They are capable of capturing both the salient spatial and temporal information in the video sequences, while removing much of the redundant information that is not pertinent to the action. Combining these features in a hierarchical fashion further increases performance.
Project Report from the year 2017 in the subject Computer Science - Programming, , language: English, abstract: This report covers the work that was carried out by a group of researchers on CNC (Computer Numerical Control) programming and machining. The task was to choose and design a creative item to be machined using CNC machining, which then required to write a code using CNC language. Prior to the machining process, we did a Computer Aided Design (CAD) drawing of the Mercedes Benz logo. The logo was further modified with the final model drawn using Auto Desk Inventor. We used foam for our model and a 10 diameter end mill tool. The main problem that was experienced was the cutting time; the model took longer to be complete. The cutting time was affected by the complexity of the design, chosen tool size and the cutting technique. We learnt from the demonstration that the shorter the constructed code the more robust it is, using a bigger tool is more efficient in terms of saving energy and time, and that if the code is correct the CNC machine model becomes identical to that of the product Design.
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