Citation
Chang, P., Hirvonen, D., Camus, T., Southall, B., (June 2005). “Stereo-Based Object Detection, Classification, and Quantitative Evaluation with Automotive Applications,” Computer Vision and Pattern Recognition – Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, vol., no., pp.62,62, 25-25.
Abstract
A real-time stereo-based pre-crash object detection and classification system is presented. The system employs a model based stereo object detection algorithm to find candidate objects from the scene, followed by a Bayesian classification framework to assign each candidate to its proper class. Our current system detects and classifies several types of objects commonly seen for automotive applications, namely vehicles, pedestrians/bikes, and poles. We describe both the detection and classification algorithms in detail along with real-time implementation issues. A quantitative analysis of performance on a static data set is also presented.
