Wenjing Shi

Wenjing Shi

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of success. Yet, the general human activity detection problem remains very challenging, especially when the methods need to work 'in the wild' (e.g., without having precise control over the imaging geometry). The thesis explores phase-based solutions for (i) detecting faces, (ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv) whether the head is looking to the left or the right, using standard video cameras without any control on the imaging geometry. The proposed phase-based approach is based on the development of simple and robust methods that rely on the use of Amplitude Modulation- Frequency Modulation (AM-FM) models. The approach is validated using video frames extracted from the Advancing Out-of-school Learning in Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265 images from ten students looking at the camera, and 6,122 images from five students looking away from the camera. For the students facing the camera, the method was able to correctly classify 97.1% of them looking to the left and 95.9% of them looking to the right. For the students facing the back of the camera, the method was able to correctly classify 87.6% of them looking to the left and 93.3% of them looking to the right. The results indicate that AM-FM based methods hold great promise for analyzing human activity videos.

Wenjing Shi, Phuong Tran, Sylvia Celedón-Pattichis et al.

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state of the art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos.

Andrew C. Freeman, Wenjing Shi, Bin Hwang

The quality of recorded videos and images is significantly influenced by the camera's field of view (FOV). In critical applications like surveillance systems and self-driving cars, an inadequate FOV can give rise to severe safety and security concerns, including car accidents and thefts due to the failure to detect individuals and objects. The conventional methods for establishing the correct FOV heavily rely on human judgment and lack automated mechanisms to assess video and image quality based on FOV. In this paper, we introduce an innovative approach that harnesses semantic line detection and classification alongside deep Hough transform to identify semantic lines, thus ensuring a suitable FOV by understanding 3D view through parallel lines. Our approach yields an effective F1 score of 0.729 on the public EgoCart dataset, coupled with a notably high median score in the line placement metric. We illustrate that our method offers a straightforward means of assessing the quality of the camera's field of view, achieving a classification accuracy of 83.8\%. This metric can serve as a proxy for evaluating the potential performance of video and image quality applications.

Wenjing Shi, Marios S. Pattichis, Sylvia Celedón-Pattichis et al.

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves.