Adaptive object tracking based on vision sensor and milli-meter wave radar

作者：Jin Lu^*; Fu Mengyin; Yang Yi

来源：Journal of Computational Information Systems, 2014, 10(8): 3283-3290.

DOI：10.12733/jcis9784

• Driver assistant system
• Information measurement
• Mean shift
• Unscented Kalman filter

The object tracking for driver assistant system by the multi-sensors fusion has drawn much attention recently. With the complexity of space alignment, a new space alignment algorithm is proposed to project the object by millimeter wave radar onto the image plane. The method needs not to estimate interior and exterior parameters between two sensors. Considering alignment result as the measurement, a new tracking algorithm based on the Mean Shift with Unscented Kalman Filter and Information Measure of Multi-Scale Image is proposed. The Unscented Kalman Filter firstly is used to predict the initial iteration position for Mean Shift algorithm. After the object position is located by Mean Shift algorithm, the size of tracking window is adjusted adaptively through the size change ratio of adjacent frame. The results show that the proposed algorithm can track accurately the object whose size is gradually increasing or decreasing, and the algorithm also reduces the iteration times, the location time and the tracking error.

• 出版日期2014
• 单位北京理工大学