Abstract
In this article, an application for object segmentation and tracking for intelligent vehicles is presented. The proposed object segmentation and tracking method is implemented by combining three stages in each frame. First, based on our previous research on a fast ground segmentation method, the present approach segments three-dimensional point clouds into ground and non-ground points. The ground segmentation is important for clustering each object in subsequent steps. From the non-ground parts, we continue to segment objects using a flood-fill algorithm in the second stage. Finally, object tracking is implemented to determine the same objects over time in the final stage. This stage is performed based on likelihood probability calculated using features of each object. Experimental results demonstrate that the proposed system shows effective, real-time performance.
Highlights
Object tracking is a multimedia application that is widely applied in intelligent vehicles[1,2] such as autonomous robots
We segmented and tracked multiple objects from the point cloud obtained by the static sensor
We proposed a fast and efficient segmentation and tracking method for multiple objects using a multichannel light detection and ranging (LIDAR) sensor
Summary
Object tracking is a multimedia application that is widely applied in intelligent vehicles[1,2] such as autonomous robots. We often employ one or more multichannel laser sensors. The laser sensor scans the surrounding environment and returns a point cloud. Autonomous mobile robots can detect their surroundings, identify navigation paths, distinguish traversable ground regions from impassable obstacles, and identify relevant areas. To enable successful navigation in outdoor environments, segmenting from a three-dimensional (3-D) point cloud is important. Many studies on object segmentation have been conducted in recent years. These methods tend to be affected by certain factors, such as terrain conditions, while having long processing times
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