Conclusion
A method for detecting and tracking objects using linear stereo vision is presented. After
reconstructing 3D points from the matching edge points extracted from stereo linear images,
a clustering algorithm based on a spectral analysis is proposed to extract clusters of points
where each cluster represents an object of the observed scene. The tracking process is
achieved using Kalman filter algorithm and nearest neighbour data association. A fusion
strategy is also proposed to resolve the problem of multiple clusters that represent a same
object. The proposed method is tested with real data in the context of objects detection and
tracking in front of a vehicle.
Objects Detection and Tracking Using Points Cloud Reconstructed from Linear Stereo Vision https://www.intechopen.com/books/current-advancements-in-stereo-vision/objects-detection-and-tracking-using-points-cloud-reconstructed-from-linear-stereo-vision
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