This paper presents a novel road curb detection method using 3D-LIDAR scanner. To detect the curbs, the ground points are separated from the pointcloud first. Then the candidate curb points are selected using three spatial cues: the elevation difference, gradient value and normal orientation. Afterwards the false curb points caused by obstacles are removed using the short-term memory technique. Next the curbs are fitted using the parabola model. Finally, the particle filter is used to smooth the curb detection result. The proposed approach was evaluated on a dataset collected by an autonomous ground vehicle driving around the Ford Research campus and downtown Dearborn. Our curb detection results are accurate and robust despite variations introduced by moving vehicles and pedestrians, static obstacles, road curvature changes, etc.

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A topical video object refers to an object, that is, frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as latent Dirichelet allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top-down probabilistic topic modeling with bottom-up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when compared with topic models without such priors.