<p> Body Posture provides enough information regarding the current state of mind of a person. This idea is used to implement a system that provides feedback to lecturers on how engaging the class has been by identifying the attentive levels of students. This is carried out using the posture information extracted with the help of Mediapipe. A novel method of extracting features are from the key points returned by Mediapipe is proposed. Geometric moments aided features classification performs better than the general distances and angles features classification. In order to extend the single person pose classification to multi person pose classification object detection is implemented. Feedback is generated regarding the entire lecture and provided as the output of the system. </p>

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This project aims to create a robot system that can accurately figure out if the throws are successful. This can help make various industrial tasks more efficient. The system uses implemented methods to process data from fisheye camera data and depth sensor data, to check the quality of the throws. The main goal is to find out if the thrown object reaches its target or not, with more advanced tasks including predicting its path when frames are lost or not tracked properly.To put the system together the Robot Operating System (ROS) was used for handling data and processing, as well as different tools and techniques, like bag files and OpenCV. A variety of methods and algorithms were used to apply background subtraction, clustering, curve fitting, marking objects and drawing the path they take in the air. The depth sensor data processing is included to make up for the limitations of 2D camera data, providing more accurate and reliable tracking of thrown objects.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第24913号 / 農博第2576号 / 新制||農||1103(附属図書館) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 飯田 訓久, 教授 近藤 直, 教授 野口 良造 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Real-time people counting is a beneficial system that covers many levels of use cases. It can help keep track of the number of people entering buildings, buses, stores, and other facilities. Knowing such information can be helpful in case of fire emergencies, preventing overcrowding in public transportation and facilities, helping people with social anxiety, and more. The use cases of such a device are endless and can significantly help society’s development. This thesis will provide research and a solution for accurate real-time people counting using two devices. Having multiple devices count the number of people passing through with good accuracy would benefit facilities with multiple exits. Two Coral Dev Boards will be used, each with its web camera. With the help of machine learning, the device will recognize the top of the head of people passing through and count them, which will later be sent to a server that counts the total amount from each device. The results varied between66.7 % and 100 % accuracy, depending on the walking speed. A fast-paced walking speed, almost running, resulted in 66.7 % accuracy. Meanwhile, a regular walking speed resulted in 80-100 % accuracy.

Stereopsis or Stereo vision is a technique that has been extensively used in computer vision these days helps to percept the 3D structure and distance of a scene from two images taken at different viewpoints, precisely the same way a human being visualizes anything using both eyes. The research involves object matching by extracting features from images and includes some preliminary tasks like camera calibration, correspondence and reconstruction of images taken by a stereo vision unit and 3D construction of an object. The main goal of this research work is to estimate the position and the orientation of a front loader bucket of an autonomous mobile robot configured in a work machine name 'Avant', which consists a stereo vision unit and several other sensors and is designed for outdoor operations like excavation. Several image features finding algorithms, including the most prominent two, SIFT and SURF has been considered for the image matching and object recognition. Both algorithms find interest points in an image in different ways which apparently accelerates the feature extraction procedure, but still the time requires for matching in both cases is left as an important issue to be resolved. As the machine requires to do some loading and unloading tasks, dust and other particles could be a major obstacle for recognizing the bucket at workspace, also it has been observed that the hydraulic arm and other equipment comes inside the FOV of the cameras which also makes the task much challenging. The concept of using markers has been considered as a solution to these problems. Moreover, the outdoor environment is very different from indoor environment and object matching is far more challenging due to some factors like light, shadows, environment, etc. that change the features inside a scene very rapidly. Although the work focuses on position and orientation estimation, optimum utilization of stereo vision like environment perception or ground modeling can be an interesting avenue of future research / <p>Validerat; 20101230 (ysko)</p>

Vehicle recognition and trajectory tracking plays an integral role in many aspects of Intelligent Transportation Systems (ITS) applications; from behavioral modeling and car-following analyses to congestion prevention, crash prediction, dynamic signal timing, and active traffic management. This dissertation aims to improve the tasks of multi-object detection and tracking (MOT) as it pertains to urban traffic by utilizing the domain knowledge of traffic flow then utilize this improvement for applications in real-time traffic performance assessment, safety evaluation, and driver behavior modeling. First, the author proposes an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. This framework introduces the concept of virtual traffic lanes representing the eight standard National Electrical Manufacturers Association (NEMA) movements within an intersection as spatio-temporal clusters utilized for movement classification and vehicle re-identification. The proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation. The results obtained for a case study and on the AI City benchmark dataset indicate the high ability of the proposed framework in obtaining reliable turn count, speed estimates, and efficiently resolving the vehicle identity switches which occur within the intersection due to detection errors and occlusion. The author then proposes the utilization of the high accuracy and granularity trajectories obtained from video inference to develop a real-time safety-based driver behavior model, which managed to effectively capture the observed driving behavior in the site of study. Finally, the developed model was implemented as an external driver model in VISSIM and managed to reproduce the observed behavior and safety conflicts in simulation, providing an effective decision-support tool to identify appropriate safety interventions that would mitigate those conflicts. The work presented in this dissertation provides an efficient end-to-end framework and blueprint for trajectory extraction from road-side traffic video data, driver behavior modeling, and their applications for real-time traffic performance and safety assessment, as well as improved modeling of safety interventions via microscopic simulation. / Doctor of Philosophy / Traffic crashes are one of the leading causes of death in the world, averaging over 3,000 deaths per day according to the World Health Organization. In the United States alone, there are around 40,000 traffic fatalities annually. Approximately, 21.5% of all traffic fatalities occur due to intersection-related crashes. Intelligent Transportation Systems (ITS) is a field of traffic engineering that aims to transform traffic systems to make safer, more coordinated, and 'smarter' use of transport networks. Vehicle recognition and trajectory tracking, the process of identifying a specific vehicle's movement through time and space, plays an integral role in many aspects of ITS applications; from understanding how people drive and modeling that behavior, to congestion prevention, on-board crash avoidance systems, adaptive signal timing, and active traffic management. This dissertation aims to bridge the gaps in the application of ITS, computer vision, and traffic flow theory and create tools that will aid in evaluating and proactively addressing traffic safety concerns at urban intersections. The author presents an efficient, real-time framework for extracting reliable vehicle trajectories from roadside cameras, then proposes a safety-based driving behavior model that succeeds in capturing the observed driving behavior. This work is concluded by implementing this model in simulation software to replicate the existing safety concerns for an area of study, allowing practitioners to accurately model the existing safety conflicts and evaluate the different operation and safety interventions that would best mitigate them to proactively prevent crashes.