A Kalman Filter Based Approach to De-noise the Stereo Vision Based Pedestrian Position Estimation

This paper presents a methodology of using Kalman filter to minimize the error in stereo vision based distance measurement data (3D position of pedestrians). In stereo vision, little point mis-correspondence leads to a very bad estimate of depth during triangulation. There are robust correspondence algorithms but all of them suffer from algorithm complexity affecting the time performance. If simple correspondence algorithms are used that gave good real time performance, then the results suffer from erroneous depth measurement. In this paper, we have applied a predictive-corrective model using Kalman filter on the erroneous depth measurement. Being applied in time domain as compared to stereo image domain, the proposed approach has much less algorithm complexity and hence gives good real-time performance. The results also show that the proposed algorithm is able to significantly reduce the measurement noise without affecting the pedestrian tracking ability.