Improving Compressed Video Using Single Lightweight Model with Temporal Fusion Module.

Video compression algorithms are commonly used to reduce the number of bits required to represent a video with a high compression ratio. However, this can result in the loss of content details and visual artifacts that affect the overall quality of the video. We propose a learning-based restoration method to address this issue, which can handle varying degrees of compression artifacts with a single model by predicting the difference between the original and compressed video frames to restore video quality.

Learning-Based Image Damage Area Detection for Old Photo Recovery.

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process.

Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks.

This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic dataset with pointing gestures from the first-person perspective.

Intelligent Lecturer Tracking and Capturing System Based on Face Detection and Wireless Sensing Technology.

In this paper, we propose an intelligent lecturer tracking and capturing (ILTC) system to automatically record course videos. Real-time and stable lecturer localization is realized by combining face detection with infrared (IR) thermal sensors, preventing detection failure caused by abrupt and rapid movements in face detection and solving the non-real-time sensing problem for IR thermal sensors. Further, the camera is panned automatically by a servo motor controlled with a microcontroller to keep the lecturer in the center of the screen.