For automotive assistance systems, on-road vehicle detection is a key challenge to forward collision warning. Along with detecting existence, determining a vehicle's orientation plays an important role in correctly predicting maneuvers. In this paper, an approach to remotely estimate vehicle orientations from monocular images is presented. The proposed system operates on a per frame basis and does not require any depth cues. Orientation estimation is performed by analyzing the position of the vehicle's rear section relative to the overall vehicle outline. Both position types are determined using a newly devised tree-structured classifier. Based on the cascaded structure by Viola and Jones, the pro posed classifier adapts itself to the problem's structure, dividing the overall problem into parts that require fewer weak learners to solve. To find partitions that simplify the classification task, a quality criterion measuring class separability is optimized using the Simulated Annealing algorithm. To further increase processing speed, the number of tree nodes to be traversed is drastically reduced by a two-staged boosting procedure, training a classifier that decides which branch to take. Experiments show the relevance and effectiveness of the proposed concepts.
Efficient monocular vehicle orientation estimation using a tree-based classifier
2011-06-01
1592220 byte
Conference paper
Electronic Resource
English
Efficient Monocular Vehicle Orientation Estimation Using a Tree-Based Classifier
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