Boosted Gabor Features Applied to Vehicle Detection

Robust vehicle detection is a challenging task given vehicles with different types, and sizes, and at different distances. This paper proposes a Boosted Gabor Features (BGF) approach for vehicle detection. The two main conventional Gabor filter design approaches are a filter bank design approach with fixed parameters even for different applications and a learning approach. In contrast, the parameters of our boosted Gabor filters, learned from examples, differ from application to application. Moreover, our boosted approach optimizes the filter parameters for every image sub-window, and the boosted filters have a large response for sub-windows containing a part of a vehicle resulting in a greatly improved performance in vehicle detection. Our vehicle detection has two basic phases in which we build a multi-resolution hypothesis-validation structure. In the vehicle hypothesis generation phase, hypothesis lists are generated for three ROIs with different resolutions using horizontal and v...

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Chong Sun, Hong Cheng, Nanning Zheng
• Keywords: Computer Vision, ICPR 2006, Robust Vehicle Detection, Vehicle Detection, Vehicle Hypothesis Generation