Parallel implementation of pedestrian tracking using multiple cues on GPGPU

Nowadays, pedestrian recognition for automotive and security applications that require accurate recognition in images taken from distant observation points is a recent challenging problem in the field of computer vision. To achieve accurate recognition, both detection and tracking must be precise. For detection, some excellent schemes suitable for pedestrian recognition from distant observation points are proposed, however, no tracking schemes can achieve sufficient performance. To construct an accurate tracking scheme suitable for pedestrian recognition from distant observation points, we propose a novel pedestrian tracking scheme using multiple cues: HSV histogram and a HOG feature. Experimental results show that the proposed scheme can properly track a target pedestrian where existing schemes fail. Moreover, we implement the proposed scheme on NVIDIA Tesla C1060 processor, one of the latest GPGPU, to achieve real-time processing of the proposed scheme. Experimental results show that computation time required for tracking of a frame by our implementation is reduced to 13.8 ms even though Intel(R) Core(TM) i7 CPU 965 @ 3.20 GHz spends 122.0 ms.