Adaptive SVM-based pixel accumulation technique for a SPAD-based lidar system

This paper proposes an adaptive support vector machine (SVM)-based pixel accumulation technique for the single-photon avalanche diode (SPAD)-based flash lidar system to greatly improve the accuracy of depth images. In this method, an adaptive incremental SVM classifier is proposed to distinguish the target and background to avoid the boundary blur caused by accumulation. With the SVM classifier, only the pixels belonging to the same target are accumulated. Then by accumulating the multiple measurements of the results of neighboring pixels, the influence of noisy photons can be reduced, and an accurate peak position of the laser signal in the histogram generated by the SPAD detector can be derived. Different from most existing classification algorithms in which the SVM model is used to recognize the target in depth, the image is trained with huge amounts of offline data, which neglect the ability to handle the changes of target's features by building a small subset of input data that contains all needed information of the trained SVM model. The new method could quickly update itself with the motion of target or light intensity changes online and definitely enhance the practicability of the proposed depth image-processing method. Experimental results show that using the proposed adaptive pixel accumulation technique, the mean squared error of depth images can be reduced by 61% compared with raw images and 44% compared with the static SVM-based method when the target moves.