dc.description.abstract | The developments of deep learning and high computing support have produced a variety of tasks in the image processing area, and lots of applications have been proposed one after another. In these tasks, object tracking is a hot topic, and it’s also one of the challenging tasks. Through a combination of object detection and tracking algorithm modules, the position of the object is selected in the continuous input image, and the object identity is marked to achieve the purpose of the object tracking problem. In addition, a large number of computing behaviors for deep learning and the development of models have also attracted attention. To smoothly deploy the image project to the edge device, integrating integer quantization research for model and embedded hardware seems to become the main topic of deep learning applications.
This article discusses object tracking and the quantization of image classification. In terms of tracking tasks, we adopt a one-stage system, which is combined with the object tracking method and the embedding branch of multi-object tracking. This structure not only improves the speed of training and inference, but the model is also simpler and more accurate than the second stage architecture. In addition, this object detection structure is adopted using the center point to describe the object location, which effectively solves the accuracy of object detection than previous bounding box solution. In addition to the one-stage object tracking architecture, this paper also adds extra cross-correlation balance and attention modules, moderately adjust modules and network structure to attempt to get over multi-task training problem and strengthen the branch of embedding identity for object tracking. In the aspect of image integer quantization, we combine the custom FPGA convolution unit and the quantized object classification model to prove the possibility of implementation in embedded hardware.
The results of these tracking experiments evaluate the difference between the performance of object tracking system and identity recognition by using different network combination structures, adjusting the embedded identity dimension, and replacing different backbone networks. The results show that under the combination of cross-correlation balance and attention mechanism, we verified that object tracking identity has been improved. Additionally, it also shows different identity dimension settings, which can’t only reduce the amount of computation but improve the tracking effect. At last, in the experiment of replacing the backbone network, we compare DLA-34 with ResNet-101 backbone network, which shows that DLA-34 has fewer parameters and better accuracy.
In the hardware quantization experiments, the results of inferring object classification based on different VGG models are obtained, and we realized the importance of quantization for image inference. | en_US |