Real-time AI Vision
Fast Contents Analysis in Compressed Videos
An object detection and tracking technique can be applicable for video contents analysis in such applications as intelligent surveillance systems and interactive broadcasting services. Most of methods utilize only raw pixel data for accurate performance (so called the 'pixel domain approach'), but they require customized hardware due to high computational complexity. On the other hand, the 'compressed domain approach', which use encoded information such as motion vectors, shows extremely fast computation time, but might have worse performance in complex scenes compared to the pixel domain approach. To overcome these limitations, my team in KAIST proposed both the dissimilarity minimization (DM) and the probabilistic spatiotemporal macroblock filtering (PSMF) algorithms as a hybrid algorithm of compressed domain and pixel domain for H.264/AVC compressed videos (SPIE 2009, LNCS 2007; view presentation). Since we make use of both partially decoded data and encoded information, the methods show reliable performance in natural scenes as well as fast computation time enough to be performed in real-time.
Dissimilarity minimization
probabilistic spatiotemporal macroblock filtering
AI Engine for Real-time Scene Understanding
Using deep learning and cloud technologies, we are actively seeking to develop an advanced AI engine for real-time scene recognition and understanding from streaming videos, which can be applied to a variety of IT industries, including surveillance system, digital media broadcasting, online shopping malls, and mobile apps.
Software
- LiveVideo: Real-time video analysis
Resources
- Technical report: Analysis of H.264/AVC Encoder Reference Software
- Conference presentation (SPIE): Real-time detection and tracking in compressed videos (1/2009)
Publications
- Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain," Proceedings of SPIE, N. Kehtarnavaz and M.F. Carlsohn, San Jose, CA, USA: SPIE, 2009, pp. 72440D-72440D-12. - View presentation and news.
- Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Moving object tracking in H.264/AVC bitstream," Lecture Notes in Computer Science, vol. 4577, 2007, pp. 483-492. - View news.
Patents
- Munchurl Kim and Wonsang You, "Apparatus and method of tracking object in bitstream," KR 20080096342 (A), October 2008.