GPU Acceleration of Multi-Object Tracking with Motion Vector Interpolation and Affine Transformation

In recent studies of object detection and tracking, neural networks have been widely used, and their accuracy has improved. However, its computational complexity is very high and requires the use of high-end GPUs. In order to achieve realtime inference on edge devices, it is necessary to reduce the computational complexity of the network by scaling it down, but this leads to a loss of accuracy. To avoid this loss of accuracy, a method has been proposed in which object detection is performed using a neural network at regular intervals, and in the frames in between, the detected object positions are interpolated using motion prediction. In this research, we propose a method to improve the accuracy of interpolation even when the camera is moving by using an affine transformation used for image stabilization. We also show its realtime computation method on Jetson TX2, one of the lowest power embedded GPUs. The proposed method enables realtime processing of object detection using Yolov5s and tracking of the detected objects at the edge.