Experimental Study on Realtime Object Tracking Stystem
Grant-in-Aid for Scientific Research (A)
|Allocation Type||Single-year Grants |
Intelligent mechanics/Mechanical systems
|Research Institution||Osaka University |
SHIRAI Yoshiaki Osaka University, Faculty of Engineering, Professor, 工学部, 教授 (50206273)
SASAKI Shigeru Fujitsu Laboratories Ltd., Multimedia Systems Laboratories, Senior Researcher, マルチメディア研究所, 主任研究員
MIURA Jun Osaka University, Faculty of Engineering, Researxh Associate, 工学部, 助手 (90219585)
KUNO Yoshinori Osaka University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (10252595)
|Project Period (FY)
1995 – 1996
Completed (Fiscal Year 1996)
|Budget Amount *help
¥12,800,000 (Direct Cost: ¥12,800,000)
Fiscal Year 1996: ¥6,200,000 (Direct Cost: ¥6,200,000)
Fiscal Year 1995: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1994: ¥5,000,000 (Direct Cost: ¥5,000,000)
|Keywords||Computer vision / Object tracking / Motion image analysis / Optical flow / DSP / Realtime image processor / Active vision / Stereo vision / 動物体追跡 / 実時間DSP資覚 / リアルタイム処理 / トラッキング / 画像処理装置|
(1) Fast and Reliable Extraction of Optical Flow
At every location, a flow vactor and its uncertainy measure are calculated from multiple constraint equations, which are obtained by convolving consecutive input images with a set of orientation-selective spatial filters.
(2) Object Tracking Based on Optical Flow
An object is tracked by updating a rectangular window circumscribing the object region which has similar flow vectors. In multiple object tracking, a tracking window is assigned to each object. When two objects overlap, the foreground object is determined by comparing the flow in the overlapping region with that in each window. We also developed a method to track an object in a complicated background by integrating information over space and time.
(3) Object Tracking Based on Optical Flow and Edges
Edges of an object are searched for around the object's motion boundary obtained from optical flow. The whole contour of the object is incrementally obtained by accumulating extracted edges. When two objects with similar motion overlap, the foreground object is determined by comparing the predicted contrours and the actual controur.
(4) Object Tracking Based on Optical Flow and Disparity
Disparity is calculated as optical flow between a pair of stereo images. An object region is extracted by Baysian inference using optical flow, disparity, and the predicted target location. The method can track an object even if the tracking with either flow vectors or disparities alone may fail.
(5) Development of Realtime Tracking System
An experimental tracking system is composed of a DSP-based realtime image processor and an active stereo camera head. The image processor, which contains multiple DSP boards working in parallel, enables complex image processing in nearly a frame rate. We succeeded in realtime object tracking using the system.
Report (4 results)
Research Products (28 results)