Research Projects

Smart TV

Recent representative results are briefly described as follows:

1)   SalAd: Saliency-driven video advertising system


2)   Cloud computing and smart TV are listed as the two most important emerging technologies in the recent years. By seamlessly integrating the emerging cloud computing and smart TV, the new multimedia service platform is able to trigger the next round of revolution in the fields of digital home services, consequently opening up a whole new world for the entire TV industry and interactive media industry. To address this challenge, this project focuses on key technologies and applications of multimedia cloud services and smart TV clients for three terminals (including TV, Tablet PC and mobile phones).

easytv1  easytv2

3)   All these studies can offer important avenues for the multimedia academic community and have great potential of commercial values in digital TV, content and entertainment industries.


4)   VLabler: Sequence multi-labeling system for video annotation.


5)   Obj!CSM: Object segmentation system based on complementary saliency maps.


6)    C-VideoAdvisor: Video advertisement automatic association system.




This project puts its main focus on the challenging research issues and key technologies about multi-camera cooperated object detection, tracking, and event analysis on large-scale surveillance video data. Overall, the long-term objective of this project is to provide key technologies and solutions for the next-generation intelligent video surveillance systems and applications.

Recent representative results are briefly described as follows:

1) Object detection and tracking

obj_dt  obj_dt2

2) Event detection

ed1 ed2


3) Multi-view human detection



4) ESur: Event detection system on surveillance videos



5) XSur: surveillance object localization and tracking system



6) Fire and smoke detection for forest and city videos

fireandsmoke1   fireandsmoke2

7) BVPMeasure: Automatic Webcam-based Human Heart Rate Measurements




In this project, our objective is to develop a series of high efficient video coding models and methods for surveillance applications. Our coding methods are designed by using the special characteristics of surveillance videos to achieve higher coding performance compared with the existing coding standards which are developed for coding generic videos.

Recent representative results are briefly described as follows:

1) BDC: Background-difference-based coding

We proposed an efficient solution called background-difference-based coding (BDC). BDC follows the traditional hybrid coding framework, but utilizes the original input frames to generate and encode the periodically updated background frame. After that, it calculates the difference frames by subtracting the reconstructed background frame from the input frames, and then codes these difference frames into the code stream.


2) AVS Transcoder: A fast and performance-maintained transcoding system

  •  High-efficiency: 2~10 times of H.264 HP
  •  Low transcoding complexity: About 5% of the state-of-the-art encoder