Efficient monocular vehicle orientation estimation using a tree-based classifier

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.