An adaptive template matching-based single object tracking algorithm with parallel acceleration

Existing template matching based visual object tracking algorithms usually require to manually update the template and have high execution cost on general embedded systems. To address these issues, an adaptive template matching-based single object tracking algorithm with parallel acceleration is proposed in this paper. In this algorithm, we propose an adaptive single object tracking algorithm framework to achieve template update online. Based on the Faster-RCNN model, we design a single object capture method to update the template. Meanwhile, we present a parallel strategy to accelerate the process of template matching. To evaluate the proposed algorithm, we use OTB benchmark to compare the performance with several state-of-the-art trackers on TX2 embedded platform. Experimental results show that the proposed method achieves a 5.9 times execution speed and 71.9% accuracy improvement over the comparison methods.