GPU-based chromatic co-occurrence matrices for tracking moving objects

Generally, a good tracking system requires a huge computation time to localize, with accuracy, the target object. For real-time tracking applications, the running time is a critical factor. In this paper, a GPU implementation of the chromatic co-occurrence matrices (CCM) tracking system is proposed. Indeed, the descriptors based on CCM help to improve the accuracy of the tracking. However, they require a long computation time. To overcome this limitation, a parallel implementation of these matrices based on GPU is incorporated to the tracker. The developed algorithm is then integrated into an embedded system to build a real-time autonomous embedded tracking system. The experimental results show a speed up of 150% in the GPU version of the tracker compared to the CPU version.